The impact of surgeon volume and hospital volume on postoperative mortality and morbidity after hip fractures: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Recently, strategies aimed at optimizing provider factors have been proposed, including regionalization of surgeries to higher volume centers, and adoption of volume standards. With limited literature investigating the impact of hospital and surgeon volume on the outcome of hip fracture repairs, we undertook a systematic review to solidify the findings and attempt to arrive at a definitive conclusion with respect to both factors. MATERIALS AND METHODS: We performed a systematic review examining the association between surgeon and hospital volume and hip fracture outcomes. To be included in the review, the study population had to include patients undergoing any hip fracture repair such as hemiarthroplasty (HA), internal fixation (ORIF) and total hip arthroplasty (THA). A total of five studies investigating surgeon volume and twelve studies investigating hospital volume were included in the study. With the exception of one study investigating both surgeon and hospital volume, volume thresholds were defined for all studies. RESULTS: Studies were variable in defining surgeon and hospital volume thresholds. Low surgeon volume was associated with a longer LOS and a higher risk of mortality, but results were contrasting with respect to postoperative complications. High volume hospitals fared better than low volume with respect to length of stay, postoperative complications and time to surgery. CONCLUSIONS: Increasing hospital volume was a more stronger predictor of postoperative outcomes as compared to surgeon volume. However, there are still few researches with respect to surgeon volume and further studies may yield a more definitive answer to this question.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".