National undergraduate surgical learning objectives: the NUSLO project
Bibliographic record
Abstract
The Canadian Undergraduate Surgical Education Committee (CUSEC) undertook a project to address variance in undergraduate surgical learning objectives among Canada's medical schools. Its aim was to compile a reasonable set of national undergraduate surgical learning objectives (NUSLOs) for all medical undergraduates and map them to the Medical Council of Canada (MCC) objectives. In phase 1, CUSEC invited Canada's 10 surgical specialty societies or associations to identify discipline-specific lists of undergraduate surgical learning objectives deemed essential for all Canada's medical students to achieve by the time of graduation. In phase 2, 8 medical students and 7 CUSEC faculty from 6 Canadian universities mapped each individual NUSLO to the corresponding MCC objectives, then to primary and secondary MCC objectives. By 2023, all 10 surgical specialty societies had derived, ratified, and submitted their discipline-specific NUSLOs, for a total of 72 major objectives, some of which had sub-objectives. All phase 1 NUSLOs were mapped to corresponding MCC objectives, with each NUSLO mapping to an average of 18 MCC objectives. Each NUSLO was then tiered to 1-2 primary MCC objectives. The NUSLOs and the NUSLO-MCC maps, now publicly posted on the CUSEC website, may serve as a foundational reference for students and teachers. They are a means by which Canada's medical schools can customize, standardize, and revise their undergraduate surgical curricula.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".