Canadian Orthopaedic Residents Perception of Their Needs in Elbow Surgery Teaching
Bibliographic record
Abstract
Introduction: The aim of this paper is to guide training program and review course curriculum planning in elbow disorders. To this end, a nationwide email survey was administered to residents’ in orthopaedic surgery training programs.Material and Methods: The survey had 12 items that examined learning needs in several domains: assessment of acute and chronic elbow disorders, treatment of elbow disorders and the perceived effectiveness of various practical skills simulation sessions. A rank order list of learning needs was created. Results: Eighty-eight of 351 residents completed the survey (25%). Ninety percent of respondents thought that a one-day course would be helpful. The majority of residents felt comfortable evaluating acute traumatic elbow disorders. Their level of comfort was lower in treatment of elective disorders, with only 4% of residents comfortable managing posterior interosseous nerve and 5% comfortable managing chronic elbow instability. Only 24% of residents were comfortable treating terrible triad injuries.Conclusions: Residents reported a need for additional education in elbow surgery; especially for elective disorders. Educational needs were clustered in several areas including surgical approaches, ligament repair, and surgical management of fracture dislocations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".