Learning objectives for thoracic surgery: developing a national standard for undergraduate medical education
Bibliographic record
Abstract
Background: The Continuing Professional Development (CPD) (Education) Committee of the Canadian Association of Thoracic Surgeons (CATS) has established a goal of describing the essential knowledge of thoracic surgery. We aimed to develop a national standardized set of undergraduate learning objectives for thoracic surgery. Methods: We obtained these learning objectives from 4 medical schools in Canada. These 4 institutions were selected to provide a broad geographical representation of medical schools of varying sizes and of both official languages. The resulting list of learning objectives underwent critical review by the CPD (Education) Committee, made up of 5 Canadian community and academic thoracic surgeons, 1 thoracic surgery fellow and 2 general surgery residents. A national survey was developed and circulated to all CATS members (n = 209). Respondents were asked to indicate on a 5-point Likert scale whether each objective should be a priority for all medical students. Results: Among 209 CATS members, 56 responded (response rate 27%). The mean length of experience in clinical practice among survey respondents was 10.6 (standard deviation 10.0) years. Respondents most commonly reported teaching or supervising medical students monthly (37.0%), followed by daily (29.6%). Eight of the 10 proposed objectives received a mean Likert score of 4/5 or higher and were selected for inclusion in the final list. A finalized list of 8 learning objectives was created, following a final review from the CATS Executive Committee. Conclusion: We developed a standardized set of learning objectives for medical students that was reflective of the core concepts within thoracic surgery.
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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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".