Surgical skills assessment during resident selection process: Survey of American cardiothoracic and Canadian cardiac surgery program directors
Bibliographic record
Abstract
Objective: Matching into cardiothoracic and cardiac surgery training programs is highly competitive. As surgical simulation becomes increasingly accessible, we present the various perspectives of program directors (PDs) on the potential assessment of surgical skills during the resident selection process. Methods: A 21-question survey was distributed to all 128 accredited American cardiothoracic (75 indirect-entry 5 + 2, 19 indirect-entry 4 + 3, and 34 direct-entry I-6 programs) and 12 Canadian direct-entry cardiac surgery residency programs. Questions were focused on respondent demographic characteristics, sentiments toward integration of surgical skills assessment in resident selection, and perceptions of residents' technical skills at different stages of training. A similar questionnaire was distributed to all 360 American and Canadian general surgery PDs given its foundation for indirect-entry cardiothoracic surgery fellowships. Data were analyzed descriptively and quantitatively. Results: Forty-nine American cardiothoracic (38%), 10 Canadian cardiac (83%), 50 American general (15%), and 10 Canadian general (59%) surgery PDs completed the survey. Cardiothoracic and cardiac surgery PDs were divided on whether surgical skill assessment should be part of the selection process (yes: 52.5%; n = 31). Although 35.6% (n = 21) believed residents were slightly underperforming at the start of training, 50.9% (n = 30) believed residents were slightly or significantly overperforming by the end. Similar patterns were seen among general surgery responses. Conclusions: Surgical skills assessment during the resident selection process is divisive among cardiothoracic and cardiac surgery PDs. Surgical skills remain largely untested before residency but are developed throughout training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".