Does operative experience during residency correlate with reported competency of recent general surgery graduates?
Bibliographic record
Abstract
BACKGROUND: Identification of attributes of residency training that predict competency would improve surgical education. We hypothesized that case experience during residency would correlate with self-reported competency of recent graduates. METHODS: Aggregate case log data of residents enrolled in 2 general surgery programs were collected over a 12-month period and stratified into Surgical Council on Resident Education (SCORE) categories. We surveyed recent (< 5 yr) residency graduates on procedural competency. Resident case volumes were correlated with survey responses by SCORE category. RESULTS: In all, 75 residents performed 11 715 operations, which were distributed by SCORE category as follows: essential-common (EC) 9935 (84.8%), essential-uncommon (EU) 889 (7.6%) and complex 891 (7.6%). Alimentary tract procedures were the most commonly performed EC (2386, 24%) and EU (504, 56.7%) procedures. The least common EC procedure was plastic surgery (4, 0.04%), and the least common EU procedure was abdomen-spleen (1, 0.1%). The questionnaire response rate was 45%. For EC procedures, self-reported competency was highest in skin and soft tissue, thoracic and head and neck (each 100%) and lowest in vascular-venous (54%), whereas for EU procedures it was highest in abdomen-general (100%) and lowest in vascular-arterial (62%). The correlation between case volume and self-reported competency was poor (R = 0.2 for EC procedures). CONCLUSION: Self-reported competency correlates poorly with operative case experience during residency. Other curriculum factors, including specific rotations and timing, balance between inpatient and outpatient surgical experience and competition for cases, may contribute to procedural competency acquisition during residency.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".