Orthopaedic surgery core curriculum: the spine
Bibliographic record
Abstract
OBJECTIVE: To develop a core curriculum for orthopaedic surgery and to conduct a national survey to assess the importance of 281 items in the curriculum. Attention was focused specifically on 24 items pertaining to the curriculum that are pertinent to the spine. STUDY DESIGN: A cross-sectional survey of a random sample of orthopaedic surgeons whose primary affiliation was non-academic, representing the provinces and territories of Canada METHODS: A questionnaire containing 281 items was developed. A random group of 131 (out of 156) orthopaedic surgeons whose primary affiliation is non-academic completed the questionnaire. The data were analysed quantitatively using average mean scores, histograms, the modified Hotelling's T2 test and the Benjimini-Hochberg procedure. RESULTS: 131 of 156 (84%) orthopaedic surgeons participated, in this study. 14 of 24 items were ranked at no less than 3 out of 4 thus suggesting that 58% of the items are important or probably important to know by the end of residency (SD< or =0.07). Residents need to learn the diagnosis and principles of managing patients with common conditions of the spine. CONCLUSIONS: The study shows, with reliable statistical evidence, that orthopaedic residents are no longer expected to be able to perform spinal fusions with proficiency on completion of residency. Is the exposure to surgical spine problems and the ability to be comfortable with operating expectations specific to the fellowship level? If so, the focus during residency or increasing accredited spine fellowships needs to be addressed to ensure that enough spine surgeons are educated to meet the future healthcare demands projected for Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".