Sustainability of an Enhanced Recovery After Surgery initiative for elective colorectal resections in a community hospital
Bibliographic record
Abstract
Background: In March 2016, an Enhanced Recovery After Surgery (ERAS) initiative was implemented for all elective colorectal resections at an urban hospital in St. John's, Newfoundland and Labrador, Canada. An ERAS coordinator supervised and enforced guideline compliance for 6 months. The aim of this study was to evaluate the sustainability of the ERAS program after supervision of guideline compliance was eliminated. Methods: Patient outcomes and guideline compliance were compared between surgeries performed under standard practice (April 2014 to March 2015) and those performed during and after the implementation of the ERAS initiative (March 2016 to August 2016 was the implementation phase and September 2016 to February 2017 was the sustainability phase). Results: Hospital length of stay decreased from 7.26 days at baseline to 5.44 days during the implementation phase of the ERAS program (p < 0.001). There was no significant difference between length of stay at baseline and during the 6-month sustainability phase of the ERAS program (7.10 d). There were no significant differences in rates of readmission or mortality during and after implementation. Rate of ileus decreased significantly from 13.8% during the implementation phase to 4.6% during the sustainability phase (p = 0.036). Total guideline compliance increased from 52.2% at baseline to 80.7% during the implementation phase (p < 0.001), and decreased to 74.7% during the sustainability phase (p < 0.001). Adherence to postoperative guidelines regressed: 79.2% in the implementation phase and 68.6% in the sustainability phase (p < 0.001). Conclusion: La durée des séjours à l’hôpital a diminué après l’adoption du programme de RAAC, lorsque le coordonnateur du programme était présent. Les méthodes de maintien des lignes directrices après leur adoption seront cruciales au succès de programmes similaires à l’avenir.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".