Influence of Training Background on Elbow Arthroplasty Case Numbers: An Analysis of the American Board of Orthopaedic Surgery Part II Oral Examination Case List Database
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate the influence of training background on the frequency and indications of elbow arthroplasty performed by early-career surgeons. METHODS: A review of the American Board of Orthopaedic Surgery Part II Oral Examination Case List database from 2010 to 2021 was completed. The number of cases performed by surgeons from each individual training background were calculated and compared with the total number of surgeons who completed each fellowship during the study period. RESULTS: Hand surgeons performed the most elbow arthroplasty cases (132, 44%), but a higher percentage of shoulder/elbow surgeons performed elbow arthroplasty in comparison (15% vs. 7%). The mean number of TEA cases performed by shoulder/elbow surgeons was significantly higher than in other subspecialties (P < 0.01). However, when comparing only surgeons who performed elbow arthroplasty during the board collection period, there was no significant difference between training backgrounds (P = 0.20). DISCUSSION: While hand surgeons performed the most elbow arthroplasty cases, a higher percentage of shoulder/elbow surgeons performed elbow arthroplasty during the study period. The high prevalence of distal humerus fracture as an indication for arthroplasty reflected a shift in indications and was not related to training background.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".