Factors Associated with Attrition and Performance Throughout Surgical Training: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Attrition within surgical training is a challenge. In the USA, attrition rates are as high as 20-26%. The factors predicting attrition are not well known. The aim of this systematic review is to identify factors that influence attrition or performance during surgical training. METHOD: The review was performed in line with PRISMA guidelines and registered with the Open Science Framework (OSF). Medline, EMBASE, PubMed and the Cochrane Central Register of Controlled Trials were searched for articles. Risk of bias was assessed using the Newcastle-Ottawa scale. Pooled estimates were calculated using random effects meta-analyses in STATA version 15 (Stata Corp Ltd). A sensitivity analysis was performed including only multi-institutional studies. RESULTS: The searches identified 3486 articles, of which 31 were included, comprising 17,407 residents. Fifteen studies were based on multi-institutional data and 16 on single-institutional data. Twenty-nine of the studies are based on US residents. The pooled estimate for overall attrition was 17% (95% CI 14-20%). Women had a significantly higher pooled attrition than men (24% vs 16%, p < 0.001). Some studies reported Hispanic residents had a higher attrition rate than non-Hispanic residents. There was no increased risk of attrition with age, marital or parental status. Factors reported to affect performance were non-white ethnicity and faculty assessment of clinical performance. Childrearing was not associated with performance. CONCLUSION: Female gender is associated with higher attrition in general surgical residency. Longitudinal studies of contemporary surgical cohorts are needed to investigate the complex multi-factorial reasons for failing to complete surgical 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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| 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) | 0.003 | 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".