Does general surgery residency prepare surgeons for community practice in British Columbia?
Bibliographic record
Abstract
BACKGROUND: Preparing surgeons for clinical practice is a challenging task for postgraduate training programs across Canada. The purpose of this study was to examine whether a single surgeon entering practice was adequately prepared by comparing the type and volume of surgical procedures experienced in the last 3 years of training with that in the first year of clinical practice. METHODS: During the last 3 years of general surgery training, I logged all procedures. In practice, the Medical Services Plan (MSP) of British Columbia tracks all procedures. Using MSP remittance reports, I compiled the procedures performed in my first year of practice. I totaled the number of procedures and broke them down into categories (general, colorectal, laparoscopic, endoscopic, hepatobiliary, oncologic, pediatric, thoracic, vascular and other). I then compared residency training with community practice. RESULTS: I logged a total of 1170 procedures in the last 3 years of residency. Of these, 452 were performed during community rotations. The procedures during residency could be broken down as follows: 392 general, 18 colorectal, 242 laparoscopic, 103 endoscopic, 85 hepatobiliary, 142 oncologic, 1 pediatric, 78 thoracic, 92 vascular and 17 other. I performed a total of 1440 procedures in the first year of practice. In practice the break down was 398 general, 15 colorectal, 101 laparoscopic, 654 endoscopic, 2 hepatobiliary, 77 oncologic, 10 pediatric, 0 thoracic, 70 vascular and 113 other. CONCLUSION: On the whole, residency provided excellent preparation for clinical practice based on my experience. Areas of potential improvement included endoscopy, pediatric surgery and "other," which comprised mostly hand 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".