Evaluation of the effectiveness of an enhanced recovery after surgery program using data from the National Surgical Quality Improvement Program
Bibliographic record
Abstract
<h3>Background:</h3> Barriers exist in implementing enhanced recovery after surgery (ERAS), which aims to decrease postoperative complication rates and length of stay, because perioperative care is varied and compliance from a multidisciplinary team is critical to success. The objectives of this project were to evaluate the effectiveness of the National Surgical Quality Improvement Program (NSQIP) database as a tool for the ongoing assessment of outcomes associated with ERAS and to evaluate ERAS as a quality-improvement strategy at a hospital-wide level. <h3>Methods:</h3> Adult patients who underwent an elective colorectal procedure at The Ottawa Hospital between March 2010 and September 2015 were included. Information on demographic characteristics, functional status, medical background, procedure details and hospital length of stay (LOS) was abstracted from the NSQIP database. We compared data on outcomes (LOS, postoperative complications, unplanned return visits to the emergency department and 30-day mortality) before and after ERAS. <h3>Results:</h3> We analyzed data for 609 patients (318 [52.2%] colon resection, 291 [47.8%] rectal resection; 190 [31.2%] before ERAS, 419 [68.8%] after ERAS). Significantly more patients were discharged within 5 days of surgery after ERAS than before (43.5% v. 29.1%, <i>p</i> < 0.05), and LOS more than 10 days was also reduced (23.7% v. 24.9%, <i>p</i> < 0.001). Implementation of ERAS was associated with an absolute reduction of 12% in postoperative complications and a significant reduction in surgical site infections among patients who underwent open procedures (<i>p</i> = 0.04). <h3>Conclusion:</h3> The introduction of an ERAS program for monitoring standardized perioperative care facilitates a data-driven approach to guide implementation of practice guidelines and establish the sustainability of ERAS protocols and data collection processes.
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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 | 0.024 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".