Orthopedic surgery core curriculum hip and knee reconstruction.
Bibliographic record
Abstract
OBJECTIVE: To develop a core curriculum for orthopedic surgery and to conduct a national survey to assess the importance of 281 curriculum items. Attention was focused on 55 topics pertaining to hip and knee reconstruction. METHODS: A 281-item curriculum was developed. We completed a content review and cross-sectional survey of a random selection of orthopedic surgeons whose primary affiliation was nonuniversity. We analyzed the data descriptively and quantitatively, using histograms, a modified Hotelling's T2 statistic with the p value determined by a permutation test, and the Benjamini- Hochberg/Yekutieli procedure. Our analyses assumed that each respondent answered questions independently of the answers of any other respondent but that the answers to different questions by the same respondent might be dependent. RESULTS: Of 156 orthopedic surgeons, 131 (84%) participated in this study. Of 55 items ranked by all respondents, 42 received an average mean score greater than 3.5/4.0, and 51 received an average mean score equal to or greater than 3.0/40 (the standard deviation for each item ranged from 0.00 to 0.08), suggesting that 92.7% of the items are important or probably important to know by the end of residency. CONCLUSION: This study demonstrates agreement that it is important to include 92.7% of the items that pertain to hip and knee reconstruction in a core curriculum for orthopedic surgery. Residency training programs may need to ensure that appropriate educational opportunities focusing on complex primary and revision surgery are available to meet the future needs of orthopedic surgeons whose primary affiliation is nonuniversity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".