The Impact of Surgeon and Hospital Factors on Length of Stay in Hospital After Colorectal Surgery
Bibliographic record
Abstract
Background Length of stay (LOS) after colorectal surgery (CRS) is a significant driver of healthcare utilization and adverse patient outcomes. There is little evidence examining the impact of surgeon and hospital factors on LOS, and the influence of fast-track surgery protocols on LOS after CRS using real world data. We aimed to determine how LOS is impacted by surgeon and hospital factors, and to evaluate the success of strategies aimed at mitigating the impact of provider factors on LOS after CRS. MethodsWe conducted a systematic review synthesizing the existing evidence for the impact of surgeon and hospital factors on LOS after CRS. We then conducted a retrospective cohort study, allowing us to assess the impact of surgeon and hospital factors on LOS after CRS, and to quantify the cumulative impact of provider factors on LOS. Finally, we investigated the effect of ERAS protocol implementation on LOS using real-world data, and accounting for preexisting trends in LOS after CRS and other important predictors of LOS. ResultsOur systematic review revealed that, based on the existing evidence, surgeon factors (volume and training) had more impact on LOS after CRS than hospital factors. However, studies were heterogeneous, and some were of lower quality. Our retrospective cohort study included 90,517 CRS patients, and found that, independent of patient and procedural factors, low surgeon volume was associated with a 20% increase in LOS. Increasing surgeon years-in-practice was also associated with increased LOS. Significant residual variability in LOS was identified, even when all available LOS predictors were accounted for. Finally, our interrupted time series determined that the introduction of formal ERAS protocols was associated with a rapid 1.05 day (13.7%) decrease in LOS – this was seen using real-world data and was durable over time. Conclusions Surgeon factors, such as volume and experience, have significant impact on LOS after CRS, but hospital factors are, overall, less impactful. There is considerable variability in LOS after CRS that may be attributable to variations in practice between surgeons. We demonstrated that ERAS protocols reduce LOS after CRS, possibly by mitigating practice variation at the provider level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".