Systematic review with meta-analysis of the impact of surgical fellowship training on patient outcomes
Bibliographic record
Abstract
BACKGROUND: The number of surgeons entering fellowship training before independent practice is increasing. This may have a negative impact on surgeons in training. The impact of fellowship training on patient outcomes is not yet known. This review aimed to investigate the impact of fellowship training in surgery on patient outcomes. METHODS: A systematic review of the literature was conducted to identify studies exploring the structural and surgeon-specific characteristics of fellowship training on patient outcomes. Data from these studies were extracted, synthesized and reported qualitatively, or quantitatively through meta-analysis. RESULTS: Twenty-three studies were included. The mortality rate for patients in centres with an affiliated fellowship programme was lower than that for centres without (odds ratio 0.86, 95 per cent c.i. 0.84 to 0.88), as was the rate of complications (odds ratio 0.90, 0.78 to 1.02). Surgeons without fellowship training converted more laparoscopic operations to open surgery than those with fellowship training (risk ratio (RR) 1.04, 95 per cent c.i. 1.03 to 1.05). Comparison of outcomes for senior surgeons versus current fellows showed no differences in rates of mortality (RR 1.00, 1.00 to 1.01), complications (RR 1.03, 0.98 to 1.08) or conversion to open surgery (RR 1.01, 1.00 to 1.01). CONCLUSION: Fellowship training appears to have a positive impact on patient outcomes.
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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 | 0.024 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".