Enhanced Recovery After Surgery for Breast Reconstruction: Pooled Meta-Analysis of 10 Observational Studies Involving 1,838 Patients
Bibliographic record
Abstract
Purpose: This study aimed to explore the effectiveness and safety of the enhanced recovery after surgery (ERAS) protocol versus traditional perioperative care programs for breast reconstruction. Methods: Three electronic databases (PubMed, EMBASE, and Cochrane Library) were searched for observational studies comparing an ERAS program with a traditional perioperative care program from database inception to May 5, 2018. Two reviewers independently screened the literature according to the inclusion and exclusion criteria, extracted the data, and evaluated study quality using the Newcastle-Ottawa Scale. Subgroup and sensitivity analyses were performed. The outcomes included length of hospital stay (LOS), complication rates, pain control, costs, emergency department visits, hospital readmission, and unplanned reoperation. Results: Ten studies were included in the meta-analysis. Compared with a conventional program, ERAS was associated with significantly decreased LOS, morphine administration (including postoperative patient-controlled analgesia usage rate and duration; intravenous morphine administration on postoperative day [POD] 0, 1, 2, and 4; total intravenous morphine administration on POD 0–3; oral morphine consumption on POD 0–4; and total postoperative oral morphine consumption), and pain scores (postoperative pain score on POD 0 and total pain score on POD 0–3). The other variables did not differ significantly. Conclusion: Our results suggest that ERAS protocols can decrease LOS and morphine equivalent dosing; therefore, further larger and better-quality studies that report bleeding amount and patient satisfaction are needed to validate our findings.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.056 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".