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      Performance of Orthopaedic Shoulder and Elbow Surgeons on a Biostatistical Knowledge Examination


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          The objective of this study is to evaluate the biostatistical interpretation abilities of fellowship trained orthopaedic surgeons.


          A cross-sectional survey was administered to orthopaedic surgeon members of the American Shoulder and Elbow Surgeons (ASES), assessing orthopaedic surgeon attitudes towards biostatistics, confidence in understanding biostatistics, and ability to interpret biostatistical measures on a multiple-choice test.


          A 4.5% response rate was achieved with 55 complete survey responses. The mean percent correct was 55.2%. Higher knowledge test scores were associated with younger age and fewer years since board exam completion ( p ≤ 0.001). Greater average number of publications per year correlated with superior statistical interpretation ( p=0.009). Respondents with higher self-reported confidence were more likely to accurately interpret results ( p ≤ 0.017). Of the respondents, 93% reported frequently using statistics to form medical opinions, 98% answered that statistical competency is important in the practice of orthopaedic surgery, and 80% were eager to continue learning biostatistics.


          It is concerning that fellowship-trained shoulder and elbow surgeons, many of whom frequently publish or are reviewing scientific literature for publication, are scoring 55.2% correctly on average on this biostatistical knowledge examination. Surgeons that are further from formal statistical knowledge training are more likely to have lower biostatistical knowledge test scores. Respondents who published at the highest rate were associated with higher scores. Continuing medical education in biostatistics may be beneficial for maintaining statistical knowledge utilised in the current literature.

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          Most cited references16

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          Medicine residents' understanding of the biostatistics and results in the medical literature.

          Physicians depend on the medical literature to keep current with clinical information. Little is known about residents' ability to understand statistical methods or how to appropriately interpret research outcomes. To evaluate residents' understanding of biostatistics and interpretation of research results. Multiprogram cross-sectional survey of internal medicine residents. Percentage of questions correct on a biostatistics/study design multiple-choice knowledge test. The survey was completed by 277 of 367 residents (75.5%) in 11 residency programs. The overall mean percentage correct on statistical knowledge and interpretation of results was 41.4% (95% confidence interval [CI], 39.7%-43.3%) vs 71.5% (95% CI, 57.5%-85.5%) for fellows and general medicine faculty with research training (P < .001). Higher scores in residents were associated with additional advanced degrees (50.0% [95% CI, 44.5%-55.5%] vs 40.1% [95% CI, 38.3%-42.0%]; P < .001); prior biostatistics training (45.2% [95% CI, 42.7%-47.8%] vs 37.9% [95% CI, 35.4%-40.3%]; P = .001); enrollment in a university-based training program (43.0% [95% CI, 41.0%-45.1%] vs 36.3% [95% CI, 32.6%-40.0%]; P = .002); and male sex (44.0% [95% CI, 41.4%-46.7%] vs 38.8% [95% CI, 36.4%-41.1%]; P = .004). On individual knowledge questions, 81.6% correctly interpreted a relative risk. Residents were less likely to know how to interpret an adjusted odds ratio from a multivariate regression analysis (37.4%) or the results of a Kaplan-Meier analysis (10.5%). Seventy-five percent indicated they did not understand all of the statistics they encountered in journal articles, but 95% felt it was important to understand these concepts to be an intelligent reader of the literature. Most residents in this study lacked the knowledge in biostatistics needed to interpret many of the results in published clinical research. Residency programs should include more effective biostatistics training in their curricula to successfully prepare residents for this important lifelong learning skill.
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            Robust misinterpretation of confidence intervals.

            Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students-all in the field of psychology-were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers' performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.
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              Multivariable analysis: a primer for readers of medical research.

              Lior Katz (2003)
              Many clinical readers, especially those uncomfortable with mathematics, treat published multivariable models as a black box, accepting the author's explanation of the results. However, multivariable analysis can be understood without undue concern for the underlying mathematics. This paper reviews the basics of multivariable analysis, including what multivariable models are, why they are used, what types exist, what assumptions underlie them, how they should be interpreted, and how they can be evaluated. A deeper understanding of multivariable models enables readers to decide for themselves how much weight to give to the results of published analyses.

                Author and article information

                Adv Orthop
                Adv Orthop
                Advances in Orthopedics
                11 September 2023
                : 2023
                : 8840263
                1University of Washington Orthopaedics and Sports Medicine, Seattle, Washington 98195, USA
                2Emory University School of Medicine Department of Orthopaedics, Atlanta, Georgia 30329, USA
                3University of Central Florida College of Medicine, Orlando, Florida 32827, USA
                4Orlando Health Jewett Orthopedic Institute, Orlando, Florida 32806, USA
                Author notes

                Academic Editor: Allen L. Carl

                Author information
                Copyright © 2023 Andrew P. Collins et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 13 June 2023
                : 31 July 2023
                : 1 September 2023
                Research Article



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