Recommendations from the ERAS® Society for standards for the development of enhanced recovery after surgery guidelines
Bibliographic record
Abstract
BACKGROUND: ERAS® Society guidelines are holistic, multidisciplinary tools designed to improve outcomes after surgery. The enhanced recovery after surgery (ERAS) approach was initially developed for colorectal surgery and has been implemented successfully across a large number of settings, resulting in improved patient outcomes. As the ERAS approach is increasingly being adopted worldwide and new guidelines are being generated for new populations, there is a need to define an ERAS® Society guideline and the methodology that should be followed in its development. METHODS: The ERAS® Society recommended approach for developing new guidelines is based on the creation of multidisciplinary guideline development groups responsible for defining topics, planning the literature search, and assessing the quality of the evidence. RESULTS: Clear definitions for the elements of an ERAS guideline involve multimodal and multidisciplinary approaches impacting on multiple patient outcomes. Recommended methodology for guideline development follows a rigorous approach with systematic identification and evaluation of evidence, and consensus-based development of recommendations. Guidelines should then be evaluated and reviewed regularly to ensure that the best and most up-to-date evidence is used consistently to support surgical patients. CONCLUSION: There is a need for a standardized, evidence-informed approach to both the development of new ERAS® Society guidelines, and the adaptation and revision of existing 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 | 0.120 | 0.339 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".