A retrospective review of general surgery training outcomes at the University of Toronto.
Bibliographic record
Abstract
BACKGROUND: Surgical educators have struggled with achieving an optimal balance between the service workload and education of surgical residents. In Ontario, a variety of factors during the past 12 years have had the net impact of reducing the clinical training experience of general surgery residents. We questioned what impact the reductions in trainee workload have had on general surgery graduates at the University of Toronto. METHODS: We evaluated graduates from the University of Toronto general surgery training program from 1995 to 2006. We compared final-year In-Training Evaluation Reports (ITERs) of trainees during this interval. For purposes of comparison, we subdivided residents into 4 groups according to year of graduation (1995-1997, 1998-2000, 2001-2003 and 2004-2006). We evaluated postgraduate "performance" by categorizing residents into 1 of 4 groups: first, residents who entered directly into general surgery practice after graduation; second, residents who entered into a certification subspecialty program of the Royal College of Physicians and Surgeons of Canada (RCPSC); third, residents who entered into a noncertification program of the RCPSC; and fourth, residents who entered into a variety of nonregulated "clinical fellowships." RESULTS: We assessed and evaluated 118 of 134 surgical trainees (88%) in this study. We included in the study graduates for whom completed ITER records were available and postgraduate training records were known and validated. The mean scores for each of the 5 evaluated residency training parameters included in the ITER (technical skills, professional attitudes, application of knowledge, teaching performance and overall performance) were not statistically different for each of the 4 graduating groups from 1995 to 2006. However, we determined that there were statistically fewer general surgery graduates (p < 0.05) who entered directly into general surgery practice in the 2004-2006 group compared with the 1998-2000 and 2001-2003 groups. The graduates from 2004 to 2006 who did not enter into general surgery practice appeared to choose a clinical fellowship. CONCLUSION: These observations may indicate that recent surgical graduates possess an acceptable skill set but may lack the clinical confidence and experience to enter directly into general surgery practice. Evidence seems to indicate that the clinical fellowship has become an unregulated surrogate extension of the training program whereby surgeons can gain additional clinical experience and surgical expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".