Impact of surgeon and hospital factors on length of stay after colorectal surgery systematic review
Bibliographic record
Abstract
BACKGROUND: Although length of stay (LOS) after colorectal surgery (CRS) is associated with worse patient and system level outcomes, the impact of surgeon and hospital-level factors on LOS after CRS has not been well investigated. The aim of this study was to synthesize the evidence for the impact of surgeon and hospital-level factors on LOS after CRS. METHODS: A comprehensive database search was conducted using terms related to LOS and CRS. Studies were included if they reported the effect of surgeon or hospital factors on LOS after elective CRS. The evidence for the effect of each surgeon and hospital factor on LOS was synthesized using vote counting by direction of effect, taking risk of bias into consideration. RESULTS: A total of 13 946 unique titles and abstracts were screened, and 69 studies met the inclusion criteria. All studies were retrospective and assessed a total of eight factors. Surgeon factors such as increasing surgeon volume, colorectal surgical specialty, and progression along a learning curve were significantly associated with decreased LOS (effect seen in 87.5 per cent, 100 per cent, and 93.3 per cent of studies respectively). In contrast, hospital factors such as hospital volume and teaching hospital status were not significantly associated with LOS. CONCLUSION: Provider-related factors were found to be significantly associated with LOS after elective CRS. In particular, surgeon-related factors related to experience specifically impacted LOS, whereas hospital-related factors did not. Understanding the mechanisms underlying these relationships may allow for tailoring of interventions to reduce LOS.
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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 | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".