Impact of sequential implementation of multimodal perioperative care pathways on colorectal surgical outcomes
Bibliographic record
Abstract
Background: Standardized care protocols offer the potential to reduce postoperative complication rates. The purpose of this study was to determine whether there was an additive benefit associated with the sequential implementation of the evidence-based surgical site infection bundle (SSIB) and enhanced recovery after surgery (ERAS) protocols for patients undergoing colorectal surgery in a community hospital. Methods: Patients at a single institution who underwent elective colorectal surgery between Apr. 1, 2011, and Dec. 31, 2015, were identified by means of American College of Surgeons National Surgical Quality Improvement Program data. Patients were stratified into 3 groups according to the protocol implementation dates: pre-SSIB/pre-ERAS (control), post-SSIB/pre-ERAS and post-SSIB/post-ERAS. Primary outcomes assessed were length of stay and wound complication rates. We used inverse proportional weighting to control for possible differences between the groups. Results: There were 368 patients included: 94 in the control group, 95 in the post-SSIB/pre-ERAS group and 179 in the post-SSIB/post-ERAS group. In the adjusted analyses, mean length of stay (control group 7.6 d, post-SSIB/post-ERAS group 5.5 d, p = 0.04) and overall wound complication rates (14.7% and 6.5%, respectively, p = 0.049) were reduced after sequential implementation of the protocols. Conclusion: Sequential implementation of quality-improvement initiatives yielded additive benefit for patients undergoing colorectal surgery in a community hospital, with a decrease in length of stay and wound complication rates. The amount of improvement attributable to either initiative is difficult to define as they were implemented sequentially. The improved outcomes were realized after the introduction of the ERAS protocol in adjusted analyses.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".