What Arthroscopic Skills Need to Be Trained Before Continuing Safe Training in the Operating Room?
Bibliographic record
Abstract
Abstract The purpose of this study was to generate consensus among experienced surgeons on “what skills a resident should possess before continuing safe training in the operating room (OR).” An online survey of 65 questions was developed and distributed to surgeons in the European community. A total of 216 responded. The survey included 15 questions regarding generic and specific skills; 16 on patient and tissue manipulation, 11 on knowledge of pathology and 6 on inspection of e-anatomical structures; 5 methods to prepare residents; and 12 on specific skills exercises. The importance of each question (arthroscopic skill) was evaluated ranging from 1 (not important at all) to 6 (very important). Chi-square test, respondent agreement, and a qualitative ranking method were determined to identify the top ranked skills (p < 0.05). The top four of general skills considered important were “anatomical knowledge,” “tissue manipulation,” “spatial perception,” and “triangulation” (all chi-square test > 134, p < 0.001, all excellent agreement > 0.85, and all “high priority” level). The top ranked 2 specific arthroscopic skills were “portal placement” and “triangulating the tip of the probe with a 30-degree scope” (chi-square test > 176, p < 0.001, excellent agreement, and assigned high priority). The online survey identified consensus on skills that are considered important for a trainee to possess before continuing training in the OR. Compared with the Canadian colleagues, the European arthroscopy community demonstrated similar ranking.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".