Opening the “Black Box” for Canadian Cardiac Surgery Residency Applicants∗
Bibliographic record
Abstract
Background: This study reports on the main criteria used by Canadian cardiac surgery residency program committees (RPCs) to select applicants and the perceptions of Canadian medical students interested in cardiac surgery. Methods: A 50-question online survey was sent to all 12 Canadian cardiac surgery RPCs. A similar 52-question online survey targeted at Canadian medical students interested in applying to cardiac surgery residency programs was distributed. Data from both surveys were analyzed using descriptive statistics. Results: A total of 62% of all cardiac surgery RPC members (66 of 106) participated, including committee members from all 12 programs (range: 1-12 members per program; 9%-100% response rate per program) and 67% of program directors (8 of 12). Forty-one Canadian medical students (22 pre-clerks [54%], 2 MD/PhD students [5%], and 17 clinical clerks [41%]) participated. Committee members considered the following criteria to be most important when selecting candidates: on-service clinical performance, the interview, quality of reference letters from cardiac surgeons, and completing a rotation at the target program's institution. In contrast, the following criteria relating to the candidate were considered to be less important: wanting to practice in the city or province of training, having a connection to the program location, and personally knowing committee members. Medical students' perceptions were concordant regarding what factors are the most important but they overestimated the influence of non-clinical factors and research productivity in increasing their competitiveness. Conclusion: Canadian cardiac surgery residency programs seek applicants who demonstrate clinical excellence, as assessed by surgical rotations and reference letters from colleagues, and strong interview performance.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".