Enhanced recovery after surgery (ERAS®) society guidelines for gynecologic oncology: Addressing implementation challenges - 2023 update
Bibliographic record
Abstract
BACKGROUND: Despite evidence supporting its use, many Enhanced Recovery After Surgery (ERAS) recommendations remain poorly adhered to and barriers to ERAS implementation persist. In this second updated ERAS® Society guideline, a consensus for optimal perioperative care in gynecologic oncology surgery is presented, with a specific emphasis on implementation challenges. METHODS: Based on the gaps identified by clinician stakeholder groups, nine implementation challenge topics were prioritized for review. A database search of publications using Embase and PubMed was performed (2018-2023). Studies on each topic were selected with emphasis on meta-analyses, randomized controlled trials, and large prospective cohort studies. These studies were then reviewed and graded by an international panel according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. RESULTS: All recommendations on ERAS implementation challenge topics are based on best available evidence. The level of evidence for each item is presented accordingly. CONCLUSIONS: The updated evidence base and recommendations for stakeholder derived ERAS implementation challenges in gynecologic oncology are presented by the ERAS® Society in this consensus review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".