Bibliographic record
Abstract
Purpose: To determine if patient outcomes and compliance with best practice guidelines improved when an Enhanced Recovery After Surgery (ERAS) program was implemented for elective colorectal resections at St. Clare’s Mercy Hospital (SCMH) in St. John’s, Newfoundland and Labrador (NL). Methods: Interrupted time-series analysis was utilized to compare patient outcomes and guideline compliance between surgeries that were performed under standard practice (April 1, 2014 to March 31, 2015) and those performed during the first year of the ERAS program (March 1, 2016 to February 28, 2017). An ERAS Coordinator supervised guideline compliance in the first six months of ERAS surgeries. Charts were manually reviewed to obtain patient outcomes and compliance with guidelines. Results: Length of stay (LOS) decreased significantly from 7.26 days in the control (standard practice) group to 6.27 days in the ERAS group. LOS was shorter in the first six months of ERAS (5.44 days) than in the second six months of ERAS (7.10 days). There were no statistically significant differences in rates of complication, readmission, or mortality with implementation of ERAS. Overall compliance with guidelines increased significantly from 52.2% to 77.7% with implementation of ERAS. Postoperative compliance decreased (79.2% to 68.6%) from the first six months to the second six months of ERAS. Conclusion: Implementation of ERAS was successful at reducing LOS, but not rates of complication, readmission, or mortality. The success of this program appears to have been largely dependent on guideline supervision by an ERAS coordinator in the first six months. Methods for ensuring postoperative compliance are vital to the success of similar programs in the future.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".