Quality of plastic surgery Enhanced Recovery After Surgery (ERAS) studies: A systematic review
Bibliographic record
Abstract
BACKGROUND: In effort to improve post-operative outcomes, enhanced recovery after surgery (ERAS) protocols have gained popularity. The objective of this systematic review was to assess the reporting and methodological quality of plastic surgery ERAS studies. METHODS: All plastic surgery ERAS implementation studies, published between January 1, 2020, to November 20, 2023, were included. The primary outcome was reporting quality based on "The Reporting on ERAS Compliance, Outcomes, and Elements Research (RECOvER) checklist" (40 points). Secondary outcomes included methodology quality as per ERAS® Society endorsed guidelines (Breast 18 points; Head and Neck (H&N) 24 points). RESULTS: Fifty ERAS studies were included (breast reconstruction: 29, 58%; head and neck: 7, 14%; craniofacial: 6, 12%; aesthetic: 5, 10%; other: 3, 6%). Average reporting quality was 22.6/40 (56.7%). ERAS protocol elements least adhered to included: patient warming strategy (8/50, 16%), management of post-operative fluids (14/50, 28%), and post-discharge outcome tracking (14/50, 28%). Evaluation of breast methodological quality revealed average compliance of 9.2/18 (51.3%). The least complied with elements included preoperative computed tomography angiography (4/23, 17.4%), intraoperative warming (6/23, 26.1%), and post-operative wound management (2/23, 8.7%). For head and neck studies, average compliance was 9.1/23 (39.6%). The least complied with elements included pre-anesthesia pain medications (1/7, 14.3%), post-operative wound care (0/7, 0%), and urinary catheterization removal (1/7, 14.3%). CONCLUSIONS: ERAS implementation studies in plastic surgery are highly variable, with overall low reporting and methodology quality. Plastic surgeons should be cautious when adopting published ERAS protocols that do not adhere to the recommended and official ERAS® Society guidelines.
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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 | 0.088 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".