ENHANCED RECOVERY AFTER SURGERY GUIDELINES’ IMPACT ON VULVAR SURGICAL OUTCOMES: A retrospective cohort study
Bibliographic record
Abstract
Objective The main objective is to analyze ERAS’s impact on vulvar oncology surgery. Design Retrospective observational cohort study Setting A single center, an academic reference center, and main referral for gynecology oncological patients in Quebec province, Canada. Population or Sample All patients who underwent surgery for vulvar cancer (or suspicious lesions) from the pre-ERAS (2015) and ERAS (2019-2021) periods were included. Methods The length of stay, postoperative complications, readmissions, reoperation and mortality rate were compared between pre-ERAS and ERAS cohorts. CHUM’s practices were compared to ERAS guidelines recommendations and ERAS Interactive Audit System (EIAS®). Proportions were used to analyze compliance with available guidelines. Main Outcome Measures The primary outcome of this study is ERAS’ impact on vulvar oncology surgery outcomes. Other outcomes include the analysis of CHUM’s compliance to ERAS guidelines and EIAS® ability to evaluate compliance for vulvar surgeries. Results 154 patients were included. Compliance increased from 28.5% % to 85.7%. We observed a reduction in length of stay (p=0.002), hospitalization complications(p=0.033) [respiratory (p=0.009), psychiatric (p=0.012) and pain (0.047)], hospitalization serious complications (p=0.011), postdischarge wound infections (p<0.001) with similar postdischarge complication rates (p=0.598). EIAS compliance algorithm only correctly interpreted 21.4% of recommendations. Conclusions ERAS implementation is beneficial regarding vulvar cancer surgery outcomes. All applicable guidelines should be grouped into a single checklist. EIAS compliance algorithm should be adapted to vulvar surgery. Funding This study received one scholarship grant from the PRogramme d’Excellence en Médecine pour l’Initiation En Recherche . Keywords ERAS, guideline, vulvar cancer, surgery, EIAS
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".