Advances in Orthopedics (Jan 2023)

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

  • Andrew P. Collins,
  • Max McCall,
  • Joshua Cassinat,
  • Alison Grise,
  • Jonathan Schwartzman,
  • John Kelly,
  • Benjamin C. Service

DOI
https://doi.org/10.1155/2023/8840263
Journal volume & issue
Vol. 2023

Abstract

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