European Enhanced Recovery After Surgery (<scp>ERAS</scp>) gynecologic oncology survey: Status of <scp>ERAS</scp> protocol implementation across Europe
Bibliographic record
Abstract
OBJECTIVE: To acquire a comprehensive assessment of the current status of implementation of Enhanced Recovery After Surgery (ERAS) protocols across Europe. METHODS: The survey was launched by The European Network of Young Gynecologic Oncologists (ENYGO). A 45-item survey was disseminated online through the European Society of Gynecological Oncology (ESGO) Network database. RESULTS: A total of 116 ESGO centers participated in the survey between December 2020 and June 2021. Overall, 80 (70%) centers reported that ERAS was implemented at their institution: 63% reported a length of stay (LOS) for advanced ovarian cancer surgery between 5 and 7 days; 57 (81%) centers reported a LOS between 2 and 4 days in patients who underwent an early-stage gynecologic cancer surgery. The ERAS items with high reported compliance (>75% "normally-always") included deep vein thrombosis prophylaxis (89%), antibiotic prophylaxis (79%), prevention of hypothermia (55%), and early mobilization (55%). The ERAS items that were poorly adhered to (less than 50%) included early removal of urinary catheter (33%), and avoidance of drains (25%). CONCLUSION: This survey shows broad implementation of ERAS protocols across Europe; however, a wide variation in adherence to the various ERAS protocol items was reported.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".