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Year : 2017  |  Volume : 8  |  Issue : 4  |  Page : 187-191

Common pitfalls in statistical analysis: Measures of agreement

1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India
2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India
3 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Date of Web Publication5-Oct-2017

Correspondence Address:
Priya Ranganathan
Department of Anaesthesiology, Tata Memorial Centre, Ernest Borges Road, Parel, Mumbai - 400 012, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/picr.PICR_123_17

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Agreement between measurements refers to the degree of concordance between two (or more) sets of measurements. Statistical methods to test agreement are used to assess inter-rater variability or to decide whether one technique for measuring a variable can substitute another. In this article, we look at statistical measures of agreement for different types of data and discuss the differences between these and those for assessing correlation.

Keywords: Agreement, biostatistics, concordance

How to cite this article:
Ranganathan P, Pramesh C S, Aggarwal R. Common pitfalls in statistical analysis: Measures of agreement. Perspect Clin Res 2017;8:187-91

How to cite this URL:
Ranganathan P, Pramesh C S, Aggarwal R. Common pitfalls in statistical analysis: Measures of agreement. Perspect Clin Res [serial online] 2017 [cited 2023 Mar 25];8:187-91. Available from: http://www.picronline.org/text.asp?2017/8/4/187/215973

   Introduction Top

Often, one is interested in knowing whether measurements made by two (sometimes more than two) different observers or by two different techniques produce similar results. This is referred to as agreement or concordance or reproducibility between measurements. Such analysis looks at pairs of measurements, either both categorical or both numeric, with each pair having been made on one individual (or a pathology slide, or an X-ray).

Superficially, these data may appear to be amenable to analysis using methods used for 2 × 2 tables (if the variable is categorical) or correlation (if numeric), which we have discussed previously in this series.[1],[2] However, a closer look would show that this is not true. In those methods, the two measurements on each individual relate to different variables (e.g., exposure and outcome, or height and weight, etc), whereas in the “agreement” studies, the two measurements relate to the same variable (e.g., chest radiographs rated by two radiologists or hemoglobin measured by two methods).

   What Is Agreement? Top

Let us consider the case of two examiners A and B evaluating answer sheets of 20 students in a class and marking each of them as “pass” or “fail,” with each examiner passing half the students. [Table 1] shows three different situations that may happen. In situ ation 1 in this table, eight students receive a “pass” grade from both the examiners, eight receive a “fail” grade from both the examiners, and four receive pass grade from one examiner but “fail” grade from the other (two passed by A and the other two by B). Thus, the two examiners' results agree for 16/20 students (agreement = 16/20 = 0.80, disagreement = 4/20 = 0.20). This seems quite good. However, this fails to take into account that some of the grades may have been guesswork and that the agreement may have occurred just by chance.
Table 1: Results of 20 students, each evaluated independently by two examiners

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Let us now consider a hypothetical situation where examiners do exactly this, i.e., assign grades by tossing a coin; heads = pass, tails = fail [Table 1], Situation 2]. In that case, one would expect 25% (=0.50 × 0.50) of students to receive pass grade from both and another 25% to receive “fail” grade from both – an overall “expected” agreement rate for “pass” or “fail” of 50% (=0.25 + 0.25 = 0.50). Hence, the observed agreement rate (80% in situ ation 1) needs to be interpreted keeping in mind that 50% agreement was expected purely by chance. These examiners could have bettered this by 50% (best possible agreement minus the agreement expected by chance = 100%−50% =50%), but achieved only 30% (observed agreement minus the agreement expected by chance = 80%−50% =30%). Thus, their real performance in being concordant is 30%/50% = 60%.

Of course, they could theoretically have performed worse than what was expected by chance. For instance, in situ ation 3 [Table 1], even though each of them passed 50% of students, their grades agreed for only 4 of the 20 students – far fewer than that expected even by chance!

It is important to note that, in each of the three situations in [Table 1], the pass percentages for the two examiners are equal, and if the two examiners are compared using a usual 2 × 2 test for paired data (McNemar's test), one would find no difference between their performances; by contrast, the inter-observer agreement in the three situations is widely different. The basic concept to be understood here is that “agreement” quantifies the concordance between the two examiners for each of the “pairs” of scores and not the similarity of the overall pass percentage between the examiners.

   Methods Used to Measure Agreement Top

The statistical methods used to assess agreement vary depending on the type of variable being studied and the number of observers between whom agreement is sought to be assessed. These are summarized in [Table 2] and discussed below.
Table 2: Methods used for assessment of agreement between observers depending on the type of variable measured and the number of observers

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   Assessing Agreement between Measurements of Categorical Variables Top

Two observers assessing the same binary outcome (Cohen's kappa)

Cohen's kappa (κ) calculates inter-observer agreement taking into account the expected agreement by chance as follows:

κ = (observed agreement [Po] – expected agreement [Pe])/(1-expected agreement [Pe]).

In the above example [Table 1], Situation 1], Cohen's k = (0.80 − 0.50)/(1 − 0.50) = 0.30/0.50 = 0.60.

The κ statistic can take values from − 1 to 1, and is interpreted somewhat arbitrarily as follows: 0 = agreement equivalent to chance; 0.10–0.20 = slight agreement; 0.21–0.40 = fair agreement; 0.41–0.60 = moderate agreement; 0.61–0.80 = substantial agreement; 0.81–0.99 = near-perfect agreement; and 1.00 = perfect agreement. Negative values indicate that the observed agreement is worse than what would be expected by chance. An alternative interpretation offered is that kappa values below 0.60 indicate a significant level of disagreement.

Cohen's κ can also be used when the same rater evaluates the same patients at two time points (say 2 weeks apart) or, in the example above, grades the same answer sheets again after 2 weeks. Its limitations are: (i) it does not take into account the magnitude of differences, making it unsuitable for ordinal data, (ii) it cannot be used if there are more than two raters, and (iii) it does not differentiate between agreement for positive and negative findings – which may be important in clinical situations (e.g., wrongly diagnosing a disease versus wrongly excluding it may have different consequences).

Variations of Cohen's κ

Weighted kappa

For ordinal data, where there are more than two categories, it is useful to know if the ratings by different raters varied by a small degree or by a large amount. For example, microbiologists may rate bacterial growth on culture plates as: none, occasional, moderate, or confluent. Here, ratings of a particular plate by two reviewers as “occasional” and “moderate,” respectively, would imply a lower level of discordance than if these ratings were “no growth” and “confluent,” respectively. The weighted Kappa statistic takes this difference into account. It thus yields a higher value when the raters' responses correspond more closely, with the maximum scores for perfect agreement; conversely, a larger difference in two ratings provides a lower value of weighted kappa. Techniques for assigning weightage to the difference between categories (linear, quadratic) can vary.

Fleiss' kappa

This method is used when ratings by more than two observers are available for either binary or ordinal data.

   Assessing Agreement between Measurements of Continuous Variables Top

Two methods are available for assessing agreement between measurements of a continuous variable across observers, instruments, time points, etc. One of these, namely intra-class correlation coefficient (ICC), provides a single measure of the extent of agreement, and the other, namely Bland–Altman plot, in addition, provides a quantitative estimate of how closely the values from two measurements lie.

Intra-class correlation coefficient

Let us think of two ophthalmologists measuring intraocular pressure using a tonometer. Each patient will thus have two readings – one by each observer. ICC provides an estimate of overall concordance between these readings. It is somewhat akin to “analysis of variance” in that it looks at the between-pair variances expressed as a proportion of the total variance of the observations (i.e., the total variability in “2n” observations, which would be expected to be the sum of within- and between-pair variances). The ICC can take a value from 0 to 1, with 0 indicating no agreement and 1 indicating perfect agreement.

Bland–Altman plots

When two instruments or techniques are used to measure the same variable on a continuous scale, the Bland–Altman plots can be used to estimate agreement. This plot is a scatter plot of the difference between the two measurements (Y-axis) against the average of the two measurements (X-axis). Thus, it provides a graphical display of bias (mean difference between the two observers or techniques) with 95% limits of agreement. The latter are given by the formula:

Limits of agreement = mean observed difference ± 1.96 × standard deviation of observed differences.

Consider a situation where we wish to assess the agreement between hemoglobin measurements (in g/dL) using a bedside hemoglobinometer and the formal photometric laboratory technique in ten persons [Table 3]. The Bland–Altman plot for these data shows the difference between the two methods for each person [Figure 1]. The mean difference between the values is 1.07 g/dL (with standard deviation of 0.36 g/dL), and the 95% limits of agreement are 0.35–1.79. What this implies is that hemoglobin level of a particular person measured by photometry could vary from that measured by the bedside method from as little as 0.35 g/dL higher to as much as 1.79 g/dL higher (this is the case for 95% of individuals; for 5% of individuals, variations could be outside these limits). This obviously means that the two techniques cannot be used as substitutes for one another. Importantly, there is no uniform criterion for what constitutes acceptable limits of agreement; this is a clinical decision and depends on the variable being measured.
Table 3: Hemoglobin measurements in ten patients using two different methods

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Figure 1: Bland–Altman plot for data shown in Table 3. The upper and lower limits of agreement are generally drawn at 1.96 (roughly 2) standard deviations (of observed inter-observer differences) above and below the line representing the mean difference (solid line); these dotted lines are expected to enclose 95% of the observed inter-observer differences

Click here to view

   Points to Remember Top

Correlation versus agreement

As alluded to above, correlation is not synonymous with agreement. Correlation refers to the presence of a relationship between two different variables, whereas agreement looks at the concordance between two measurements of one variable. Two sets of observations, which are highly correlated, may have poor agreement; however, if the two sets of values agree, they will surely be highly correlated. For instance, in the hemoglobin example, even though the agreement is poor, the correlation coefficient between values from the two methods is high [Figure 2]; (r = 0.98). The other way to look at it is that, though the individual dots are not fairly close to the dotted line (least square line;[2] indicating good correlation), these are quite far from the solid black line, which represents the line of perfect agreement ([Figure 2]: the solid black line). In case of good agreement, the dots would be expected to fall on or near this (the solid black) line.
Figure 2: Scatter plot showing correlation between hemoglobin measurements from two methods for data shown in Table 3 and Figure 1. The dotted line is a trend line (least squares line) through the observed values, and the correlation coefficient is 0.98. However, the individual dots are far away from the line of perfect agreement (solid black line)

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Use of paired tests to assess agreement

For all the three situations shown in [Table 1], the use of McNemar's test (meant for comparing paired categorical data) would show no difference. However, this cannot be interpreted as an evidence of agreement. The McNemar's test compares overall proportions; therefore, any situation where the overall proportion of pass/fail by the two examiners is similar (e.g., situations 1, 2, and 3 in [Table 1]) would result in a lack of difference. Similarly, the paired t-test compares mean difference between two observations in a group. It can therefore be nonsignificant if the average difference between the paired values is small, even though the differences between two observers for individuals are large.

   Suggested Reading Top

The readers are referred to the following papers that feature measures of agreement:

  1. Qureshi et al. compared the grade of prostatic adenocarcinoma as assessed by seven pathologists using a standard system (Gleason's score).[3] Concordance between each pathologist and the original report and between pairs of pathologists was determined using Cohen's kappa. It is a useful example. However, we feel that, Gleason's score being an ordinal variable, weighted kappa might have been a more appropriate choice
  2. Carlsson et al. looked at inter- and intra-observer variability in Hand Eczema Extent Score in patients with hand eczema.[4] Inter- and intra-observer reliability was assessed using the ICC
  3. Kalantri et al. looked at the accuracy and reliability of pallor as a tool for detecting anemia.[5] They concluded that “Clinical assessment of pallor can rule out and modestly rule in severe anemia.” However, the inter-observer agreement for detecting pallor was very poor (kappa values = 0.07 for conjunctival pallor and 0.20 for tongue pallor) which means that pallor is an unreliable sign for diagnosing anemia.

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Conflicts of interest

There are no conflicts of interest.

   References Top

Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: Logistic regression. Perspect Clin Res 2017;8:148-51.  Back to cited text no. 1
  [Full text]  
Aggarwal R, Ranganathan P. Common pitfalls in statistical analysis: The use of correlation techniques. Perspect Clin Res 2016;7:187-90.  Back to cited text no. 2
[PUBMED]  [Full text]  
Qureshi A, Lakhtakia R, AL Bahri M, Al Haddabi I, Saparamadu A, Shalaby A, et al. Gleason's grading of prostatic adenocarcinoma: Inter-observer variation among seven pathologists at a tertiary care center in Oman. Asian Pac J Cancer Prev 2016;17:4867-8.  Back to cited text no. 3
Carlsson A, Svensson Š, Anderson CD, Baranovskaya I, Hindsén-Stenström M, Holt I, et al. Scoring of hand eczema: Good reliability of hand eczema extent score (HEES). Acta Derm Venereol 2017;97:193-7.  Back to cited text no. 4
Kalantri A, Karambelkar M, Joshi R, Kalantri S, Jajoo U. Accuracy and reliability of pallor for detecting anaemia: A hospital-based diagnostic accuracy study. PLoS One 2010;5:e8545.  Back to cited text no. 5


  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3]

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Madhurima Basu, Smartya Pulai, Subhasis Neogi, Mainak Banerjee, Nitai P Bhattacharyya, Sanghamitra Sengupta, Pradip Mukhopadhyay, Arpita Ray Chaudhury, Sujoy Ghosh
BMJ Open Diabetes Research & Care. 2022; 10(6): e003058
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47 Development of a new version of the Bristol Stool Form Scale: translation, content validity, face validity, and reliability of the Persian version
Nasim Shokouhi, Samira Mohammadi, Zeenat Ghanbari, Ali Montazeri
BMJ Open Gastroenterology. 2022; 9(1): e001017
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48 Examination of New Electrocardiographic Repolarization Markers in Diabetic Patients with Noncritical Coronary Artery Disease
Müjgan Gürler, Mehmet Inanir, Anca Daniela Farcas
International Journal of Clinical Practice. 2022; 2022: 1
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49 Evaluation of methods to distinguish trips from activities in walking and cycling GPS data
Elmira Berjisian, Alexander Bigazzi
Transportation Research Part C: Emerging Technologies. 2022; 137: 103588
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50 How is vowel production in Italian affected by geminate consonants and stress patterns?
Journal of Child Language. 2022; : 1
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51 A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA
Samuele Bovo, Valerio Joe Utzeri, Anisa Ribani, Valeria Taurisano, Giuseppina Schiavo, Luca Fontanesi
Scientific Reports. 2022; 12(1)
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52 Reducing gender bias in household consumption data: Implications for food fortification policy
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53 Masseter muscle volume as a disease marker in adult-onset myotonic dystrophy type 1
Agata Oliwa, Clarissa Hocking, Mark J Hamilton, John McLean, Sarah Cumming, Bob Ballantyne, Ravi Jampana, Cheryl Longman, Darren G Monckton, Maria Elena Farrugia
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54 Subtyping intestinal metaplasia in patients with chronic atrophic gastritis: an interobserver variability study
Julia M. Lerch, Rish K. Pai, Ian Brown, Anthony J. Gill, Dhanpat Jain, Bence Kovári, Ryoji Kushima, Kieran Sheahan, Tomas Slavik, Amitabh Srivastava, Gregory Y. Lauwers, Cord Langner
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55 Spatial covariance analysis of FDG-PET and HMPAO-SPECT for the differential diagnosis of dementia with Lewy bodies and Alzheimer's disease
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56 Evaluating Agreement between Clinic and Patient-Reported Outcomes for Weight and Comorbidities at 1 Year Following Bariatric Surgery
Arielle Grieco, Meridith E. Greene, Clifford Y. Ko, Mark E. Cohen, Kimberly Evans-Labok, Teresa Fraker, Matthew M. Hutter
Surgery for Obesity and Related Diseases. 2022;
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57 Just teasing! - Infants' and toddlers' understanding of teasing interactions and its effect on social bonding
Livia Colle, Gerlind Grosse, Tanya Behne, Michael Tomasello
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58 CT measures of femoral and tibial version and rotational position of femoral and tibial components of knee replacements: limitations in reliability and suitability for routine clinical practice
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59 A standardized method to measure the membranous urethral length (MUL) on MRI of the prostate with high inter- and intra-observer agreement
Hans Veerman, Marinus J. Hagens, Caroline M. Hoeks, Henk G. van der Poel, Pim J. van Leeuwen, André N. Vis, Stijn W. T. J. P. Heijmink, Ivo G. Schoots, Margriet C. de Haan, Thierry N. Boellaard
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60 Long-term Reported Outcomes Following Primary Laparoscopic Sleeve Gastrectomy
Nasser Sakran, Kim Soifer, Keren Hod, Shiri Sherf-Dagan, Sharon Soued, Yafit Kessler, Dana Adelson, Reut Biton, J. N. Buchwald, David Goitein, Asnat Raziel
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61 Effectiveness of Mobile-Based Mindfulness Interventions in Improving Mindfulness Skills and Psychological Outcomes for Adults: a Systematic Review and Meta-regression
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62 Are We Agreed? Self- Versus Proxy-Reporting of Paediatric Health-Related Quality of Life (HRQoL) Using Generic Preference-Based Measures: A Systematic Review and Meta-Analysis
Diana Khanna, Jyoti Khadka, Christine Mpundu-Kaambwa, Kiri Lay, Remo Russo, Julie Ratcliffe, Nancy Devlin, Richard Norman, Rosalie Viney, Julie Ratcliffe, Kim Dalziel, Brendan Mulhern, Harriet Hiscock, Deborah Street, Gang Chen, Tessa Peasgood, Cate Bailey, Christine Mpundu-Kaambwa, Alice Yu, Mina Bahrampour, Renee Jones, Rachel O’Loughlin, Yiting Luo, Alex van Heusden, Xiuqin Xiong, Diana Khanna, Ashwini De Silva
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63 Ecological Momentary Assessment: A Systematic Review of Validity Research
Lesleigh Stinson, Yunchao Liu, Jesse Dallery
Perspectives on Behavior Science. 2022;
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64 Managing Boundary Uncertainty in Diagnosing the Patients of Rural Area Using Fuzzy and Rough Set
Sayan Das, Jaya Sil
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65 Exploring Worker Experience as a Predictor of Routine and Non-routine Safety Performance Outcomes in the Mining Industry
Cassandra L. Hoebbel, Emily J. Haas, Margaret E. Ryan
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66 Agreement and Correlation Between Different Topical Corticosteroid Potency Classification Systems
Alexa C. Bowie, Mina Tadrous, Alexander Egeberg, Jane Harvey, Stephanie J. Lax, Jacob P. Thyssen, Aaron M. Drucker
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67 Transvaginal Two-Dimensional Ultrasound Evaluation as a Screening Tool for Levator ani Muscle Avulsion in Postpartum Women
Yue Wang, HuiFang Wang
Journal of Ultrasound in Medicine. 2022;
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68 Limitations of laparoscopy to assess the peritoneal cancer index and eligibility for cytoreductive surgery with HIPEC in peritoneal metastasis
Can Yurttas, Lisa Überrück, Giorgi Nadiradze, Alfred Königsrainer, Philipp Horvath
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69 External validation of the deep learning system “SpineNet” for grading radiological features of degeneration on MRIs of the lumbar spine
Alexandra Grob, Markus Loibl, Amir Jamaludin, Sebastian Winklhofer, Jeremy C. T. Fairbank, Tamás Fekete, François Porchet, Anne F. Mannion
European Spine Journal. 2022;
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70 Ultrasonography: an interesting imaging method for ligament assessment during the acute phase of closed elbow injuries
Romain Bilger, Pierre Laumonerie, Hugo Barret, Franck Lapègue, Pierre Mansat, Nicolas Sans, Marie Faruch-Bilfeld
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71 An Evaluation of Parent and Teacher Discrepancies on an Adaptive Behavior Measure for Youth with Autism Spectrum Disorder, Intellectual Disability, and Global Developmental Delay
Mallory A. Stevens, Kimberly J. Selders, Olivia Jeckel, Valerie Brownfield, Kerri P. Nowell
Journal of Autism and Developmental Disorders. 2022;
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72 Evaluation of the quality of ‘do not use’ medication abbreviation audits: a key enabler to successful implementation of audit and feedback
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International Journal of Clinical Pharmacy. 2021;
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73 Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning
Lucas Costa, Sudip Kunwar, Yiannis Ampatzidis, Ute Albrecht
Precision Agriculture. 2021;
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74 Prognostic and predictive impact of stroma cells defined by PDGFRb expression in early breast cancer: results from the randomized SweBCG91RT trial
Carina Strell,Axel Stenmark Tullberg,Reidunn Jetne Edelmann,Lars Andreas Akslen,Per Malmström,Mårten Fernö,Erik Holmberg,Arne Östman,Per Karlsson
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75 Correlation does not imply agreement: A cautionary tale for researchers and reviewers
Christopher Edwards,Heather Allen,Crispen Chamunyonga
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76 The Rosenberg view and coronal stress radiographs give similar measurements of articular cartilage height in knees with osteoarthritis
Jacob F. Mortensen,Andreas Kappel,Lasse E. Rasmussen,Svend E. Østgaard,Anders Odgaard
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77 The reliability of radiographic measures of total ankle replacement position: an analysis from the OARS cohort
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78 Comparing video observation to electronic topography device as a method for measuring cigarette puffing behavior
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79 MRI in the Assessment of TMJ-Arthritis in Children with JIA; Repeatability of a Newly Devised Scoring System
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Academic Radiology. 2021;
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80 Detection of EGFR mutations in liquid biopsy samples using allele-specific quantitative PCR: A comparative real-world evaluation of two popular diagnostic systems
Adam Szpechcinski,Maciej Bryl,Piotr Wojcik,Grzegorz Czyzewicz,Emil Wojda,Piotr Rudzinski,Katarzyna Duk,Joanna Moes-Sosnowska,Krystyna Maszkowska-Kopij,Renata Langfort,Aleksander Barinow-Wojewodzki,Joanna Chorostowska-Wynimko
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81 Incorporating personal-device-based point-of-care ultrasound into obstetric care: a validation study
Cecilia B. Leggett, Mariam Naqvi, Tania F. Esakoff, Marcio A. Diniz, Melissa S. Wong
American Journal of Obstetrics and Gynecology. 2021;
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82 Absence of relevant clinical effects of SARS-COV-2 on the affinity of hemoglobin for O2 in patients with COVID-19
Sergi Pascual-Guàrdia, Antoni Ferrer, Óscar Díaz, Antonio O. Caguana, Elvira Tejedor, Salomé Bellido-Calduch, Diego A. Rodríguez-Chiaradia, Joaquim Gea
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83 Ausencia de efectos clínicos destacables del SARS-CoV-2 sobre la afinidad de la hemoglobina por el O2 en pacientes con COVID-19
Sergi Pascual-Guàrdia,Antoni Ferrer,Óscar Díaz,Antonio O. Caguana,Elvira Tejedor,Salomé Bellido-Calduch,Diego A. Rodríguez-Chiaradia,Joaquim Gea
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84 Teledermatología en Chile: experiencia de su implementación temprana
Diego Aragón-Caqueo,Montserrat Arceu,Gonzalo Aragón-Caqueo,Kathalina Zamora,Denis Tom,José Luis Gatica
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85 Minor migrants’ age estimation: Comparison of two dental methods
Francesco Lupariello,Alessandro Gabriele,Federica Mirri,Giuliana Mattioda,Emilio Nuzzolese,Giancarlo Di Vella
Forensic Science International: Synergy. 2021; 3: 100134
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86 Development and Initial Evaluation of Psychometric Properties of a Pain Competence Assessment Tool (PCAT)
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87 Clinical Simulation Model of Fibrinogen Decline During Hemorrhage in Major Noncardiac Surgery
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88 Wearable technology-delivered lifestyle intervention amongst adults with overweight and obese: A systematic review and meta-regression
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International Journal of Nursing Studies. 2021; : 104163
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89 Cariprazine and akathisia, restlessness, and extrapyramidal symptoms in patients with bipolar depression
Leslie Citrome,Lakshmi N. Yatham,Mehul D. Patel,Ágota Barabássy,Arlene Hankinson,Willie R. Earley
Journal of Affective Disorders. 2021; 288: 191
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90 Beyond Percent Correct: Measuring Change in Individual Picture Naming Ability
Grant M. Walker, Alexandra Basilakos, Julius Fridriksson, Gregory Hickok
Journal of Speech, Language, and Hearing Research. 2021; : 1
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91 Functional Communication Abilities in Youth With Cerebral Palsy: Association With Impairment Profiles and School-Based Therapy Goals
Clare Koopmans, Ashley Sakash, Jennifer Soriano, Helen L. Long, Katherine C. Hustad
Language, Speech, and Hearing Services in Schools. 2021; : 1
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92 Disentangling the Psycholinguistic Loci of Anomia with Cognitive Psychometric Models
Grant M. Walker
Seminars in Speech and Language. 2021; 42(03): 256
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93 Accuracy and Safety of Image-Guided Freehand Pin Placement in Canine Cadaveric Vertebrae
Eva S. Samer,Franck Forterre,Justus M.K. Rathmann,Veronika M. Stein,Christina M. Precht,Julien Guevar
Veterinary and Comparative Orthopaedics and Traumatology. 2021;
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94 Are subjective measures the answer to assess physical inactivity on a daily basis in patients with resistant hypertension?
Susana Lopes,José Mesquita-Bastos,Catarina Garcia,Daniela Figueiredo,Pedro Carvalho,José Oliveira,Jorge Polónia,Alberto Jorge Alves,Fernando Ribeiro
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95 Bioelectrical impedance analysis versus quantitative computer tomography and anthropometry for the assessment of body composition parameters in China
Qian Qin,Yang Yang,Jingfeng Chen,Yaojun Jiang,Ang Li,Meng Huang,Yihan Dong,Shoujun Wang,Suying Ding
Scientific Reports. 2021; 11(1)
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96 Livelayer: a semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images
Mansooreh Montazerin,Zahra Sajjadifar,Elias Khalili Pour,Hamid Riazi-Esfahani,Tahereh Mahmoudi,Hossein Rabbani,Hossein Movahedian,Alireza Dehghani,Mohammadreza Akhlaghi,Rahele Kafieh
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97 Interclinician agreement on the recognition of selected respiratory clinical signs in dogs and cats with abnormal breathing patterns
M. Domínguez-Ruiz, C.R. Reinero, A. Vientos-Plotts, M.E. Grobman, D. Silverstein, K. Le Boedec
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98 High PDGFRb Expression Predicts Resistance to Radiotherapy in DCIS within the SweDCIS Randomized Trial
Carina Strell,Dick Folkvaljon,Erik Holmberg,Aglaia Schiza,Viktoria Thurfjell,Per Karlsson,Jonas Bergh,Troy Bremer,Lars A. Akslen,Fredrik Wärnberg,Arne Östman
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99 Early perceptions and behavioural responses during the COVID-19 pandemic: a cross-sectional survey of UK adults
Christina Atchison,Leigh Robert Bowman,Charlotte Vrinten,Rozlyn Redd,Philippa Pristerà,Jeffrey Eaton,Helen Ward
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100 Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia
Jessica Quah,Charlene Jin Yee Liew,Lin Zou,Xuan Han Koh,Rayan Alsuwaigh,Venkataraman Narayan,Tian Yi Lu,Clarence Ngoh,Zhiyu Wang,Juan Zhen Koh,Christine Ang,Zhiyan Fu,Han Leong Goh
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101 Primer on binary logistic regression
Jenine K Harris
Family Medicine and Community Health. 2021; 9(Suppl 1): e001290
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102 A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning
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103 Autism spectrum disorder and obstetric optimality: a twin study and meta-analysis of sibling studies
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104 Validation of a novel patient-operated device for measuring skin barrier function in atopic dermatitis
Erin E. Grinich,Christina Topham,Dylan Haynes,Janice Chung,Emile Latour,Eric L. Simpson
Skin Research and Technology. 2021;
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105 Dynamics of methylated cell-free DNA in the urine of non-small cell lung cancer patients
Sander Bach,Birgit M.M. Wever,Mark A. van de Wiel,Joris D. Veltman,Sayed M.S. Hashemi,Geert Kazemier,Idris Bahce,Renske D.M. Steenbergen
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106 Validation of the telephone-based assessment of locomotion ability after stroke
Patrick R. Avelino,Lucas R. Nascimento,Kênia K.P. Menezes,Ruani A. Tenório,Gabriela N. Cândido,Isabela S. Christovão,Luci F. Teixeira-Salmela
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107 A Novel Application of the Modified Angoff Method to Rate Case Difficulty in Simulation-Based Research
Melissa N. Joseph, Jungsoo Chang, Samuel G. Buck, Marc A. Auerbach, Ambrose H. Wong, Thomas D. Beardsley, Philip M. Reeves, Jessica M. Ray, Leigh V. Evans
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108 Karnofsky Performance Score—Failure to Thrive as a Frailty Proxy?
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109 Evaluating the influence of action- and subject-specific factors on chimpanzee action copying
Alba Motes-Rodrigo,Roger Mundry,Josep Call,Claudio Tennie
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110 Phone-based monitoring to evaluate health policy and program implementation in Kenya
Paul G Ashigbie,Peter C Rockers,Richard O Laing,Howard J Cabral,Monica A Onyango,John Mboya,Daniella Arends,Veronika J Wirtz
Health Policy and Planning. 2021;
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111 Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor
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112 What’s in a name? The meaning of sustainability to destination managers
Julia N. Albrecht,Marco Haid,Wiebke Finkler,Peter Heimerl
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113 A smartphone application to objectively monitor music listening habits in adolescents
Danique E. Paping,Jantien L. Vroegop,Simone P. C. Koenraads,Carlijn M.P. le Clercq,André Goedegebure,Robert J. Baatenburg de Jong,Marc P. van der Schroeff
Journal of Otolaryngology - Head & Neck Surgery. 2021; 50(1)
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114 Self-collected and clinician-collected anal swabs show modest agreement for HPV genotyping
Racheal S. Dube Mandishora,Trine B. Rounge,Megan Fitzpatrick,Irene Kraus Christiansen,Ole Herman Ambur,Sonja Lagström,Babill Stray-Pedersen,Massimo Tommasino,Joel Palefsky,Zvavahera M. Chirenje,Michael Scheurer
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115 Cardiothoracic ratio measurement using artificial intelligence: observer and method validation studies
Pairash Saiviroonporn,Kanchanaporn Rodbangyang,Trongtum Tongdee,Warasinee Chaisangmongkon,Pakorn Yodprom,Thanogchai Siriapisith,Suwimon Wonglaksanapimon,Phakphoom Thiravit
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116 Relationship between women’s decision-making power over their own health care and use of modern contraception in the Democratic Republic of the Congo: a cross-sectional secondary data analysis
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117 Validating Ellipsoid Zone Area Measurement With Multimodal Imaging in Choroideremia
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118 An Intervention-Related Comparison of Preschoolers’ Scores on the TGMD-2 and TGMD-3
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Perceptual and Motor Skills. 2021; 128(4): 1354
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119 Response to Letter to the Editor Titled Remote Fiberoptic Nasopharyngolaryngoscopy in the Pediatric Population: Methodological Issues on Reliability and Accuracy
Lauren E. Miller,Adva Buzi,Rachel S. Rogers,Lisa M. Elden
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120 Characteristics of Acute Childhood Illness Apps for Parents: Environmental Scan
James Benoit, Lisa Hartling, Michelle Chan, Shannon Scott
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121 Impact of Guidelines on Hypertension Control in the Elderly
Del Pinto Rita, Mirela Dobre, Silvia Pagliacci, Claudio Ferri
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122 A Critical Overview of Systematic Reviews of Chemotherapy for Advanced and Locally Advanced Pancreatic Cancer using both AMSTAR2 and ROBIS as Quality Assessment Tools
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123 Efficacy and Safety of EGFR Inhibitors in the Treatment of EGFRPositive NSCLC Patients: A Meta-Analysis
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124 Inhabiting the Body(ies) in Female Soccer Players: The Protective Role of Positive Body Image
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125 Galectin-3 Binding Protein, Depression, and Younger Age Were Independently Associated With Alexithymia in Adult Patients With Type 1 Diabetes
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126 Development of a Colic Scoring System to Predict Outcome in Horses
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127 Assessment of a Rapid Semi-Quantitative Immunochromatographic Test for the Evaluation of Transfer of Passive Immunity in Calves
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128 Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
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129 Inter-Reader Agreement of Diffusion-Weighted Magnetic Resonance Imaging for Breast Cancer Detection: A Multi-Reader Retrospective Study
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130 A Functional Network Model of the Metastasis Suppressor PEBP1/RKIP and Its Regulators in Breast Cancer Cells
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131 Prevalence and Associated Factors of Psychological Distress and Burnout among Medical Students: Findings from Two Campuses
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132 Myelin Quantification in White Matter Pathology of Progressive Multiple Sclerosis Post-Mortem Brain Samples: A New Approach for Quantifying Remyelination
Marije J. D. Huitema, Eva M. M. Strijbis, Antonio Luchicchi, John G. J. M. Bol, Jason R. Plemel, Jeroen J. G. Geurts, Geert J. Schenk
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133 Smart Determination of Gold Content in PCBs of Waste Mobile Phones by Coupling of XRF and AAS Techniques
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134 Genetik Algoritma Yaklasimiyla Öznitelik Seçimi Kullanilarak Makine Ögrenmesi Algoritmalari ile Kalp Hastaligi Tahmini
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153 Assessing the Importance and Cognition Level of ACCE’s Student Learning Outcomes: Industry, Educator, and Student Perceptions
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155 Disc-fovea Angle Is Associated With Visual Field Defect Location in Patients With Glaucoma
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176 Methodological aspects when assessing the inter-rater reliability of medication error classification – A response to a letter to editor
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180 Classifications in Brief
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181 Reply to “Diagnostic Value of CT in Detecting Peripheral Zone Prostate Cancer: Methodologic Issues to Avoid Misinterpretation”
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183 Investigation of Cervical Tumor Biopsies for Chromosomal Loss of Heterozygosity (LOH) and Microsatellite Instability (MSI) at the HLA II Locus in HIV-1/HPV Co-infected Women
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