Development of an Enhanced Recovery After Surgery Surgical Safety Checklist Through a Modified Delphi Process
Bibliographic record
Abstract
Importance: Enhanced Recovery After Surgery (ERAS) guidelines and the World Health Organization Surgical Safety Checklist (SSC) are 2 well-established tools for optimizing patient outcomes perioperatively. Objective: To integrate the 2 tools to facilitate key perioperative decision-making. Evidence Review: Snowball sampling recruited international ERAS users from multiple clinical specialties. A 3-round modified Delphi consensus model was used to evaluate 27 colorectal or gynecologic oncology ERAS recommendations for appropriateness to include in an ERAS SSC. Items attaining potential consensus (65%-69% agreement) or consensus (≥70% agreement) were used to develop ERAS-specific SSC prompts. These proposed prompts were evaluated in a second round by the panelists with regard to inclusion, modification, or exclusion. A final round of interactive discussion using quantitative consensus and qualitative comments was used to produce an ERAS-specific SSC. The panel of ERAS experts included surgeons, anesthesiologists, and nurses within diverse practice settings from 19 countries. Final analysis was conducted in May 2022. Findings: Round 1 was completed by 105 experts from 18 countries. Eleven ERAS components met criteria for development into an SSC prompt. Round 2 was completed by 88 experts. There was universal consensus (≥70% agreement) to include all 37 proposed prompts within the 3-part ERAS-specific SSC (used prior to induction of anesthesia, skin incision, and leaving the operating theater). A third round of qualitative comment review and expert discussion was used to produce a final ERAS-specific SSC that expands on the current WHO SSC to include discussion of analgesia strategies, nausea prevention, appropriate fasting, fluid management, anesthetic protocols, appropriate skin preparation, deep vein thrombosis prophylaxis, hypothermia prevention, use of foley catheters, and surgical access. The final products of this work included an ERAS-specific SSC ready for implementation and a set of recommendations to integrate ERAS elements into existing SSCs. Conclusions and Relevance: The SSC could be modified to align with ERAS recommendations for patients undergoing major surgery within an ERAS protocol. The stakeholder- and expert-generated ERAS SSC could be adopted directly, or the recommendations for modification could be applied to an existing institutional SSC to facilitate implementation.
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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 | 0.238 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".