Enhanced Recovery After Surgery in Immediate DIEP Flap Breast Reconstruction: Reducing Length of Stay and Opioid Use
Bibliographic record
Abstract
Background: To improve patient outcomes amid reduced healthcare resources during the COVID-19 pandemic, a single Canadian cancer center implemented an Enhanced Recovery After Surgery (ERAS) protocol for autologous DIEP flap breast reconstruction. Methods: This retrospective cohort study included 100 consecutive patients undergoing microsurgical breast reconstruction with DIEP flaps using the ERAS protocol and 100 patients using a standard protocol. Primary outcomes were the hospital length of stay and opioid use. Secondary outcomes included postoperative complications, laxative and antiemetic consumption. Results: In this study, 80% of the patients had immediate reconstruction, while the remaining patients received either delayed immediate or delayed reconstruction. Patients in the ERAS group had shorter hospital stays (2.8 vs 4.5 days; P < 0.001) and lower total opioid use (50.2 vs 136.3 mg; P < 0.001). This reduction was also observed when breaking down opiates per day of hospitalization (30.2 vs 18.2 mg; P < 0.001), and in the first 24 postoperatively hours (35.7 vs 67.6 mg; P < 0.001). The control group had a higher incidence of postoperative complications, including seroma, partial and total flap necrosis, compared to the ERAS group. However, readmission rates were similar between the two groups. Conclusion: Implementing the ERAS protocol for DIEP flap breast reconstruction can significantly reduce hospital length of stay and postoperative opioid requirements without increasing the risk of adverse events. This pattern holds true for immediate reconstructions with DIEP flaps.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".