Enhanced Recovery After Surgery (ERAS<sup>®</sup>) in Individuals with Diabetes: A Systematic Review
Bibliographic record
Abstract
Abstract Background Prevalence of diabetes in surgical patients is 10–40%. It is well recognized that they have higher rates of complications, and longer stays in hospital compared to patients without diabetes. Enhanced recovery after surgery (ERAS) is an evidence‐based multimodal surgical care pathway that improves postoperative complications and length of stay in patients without diabetes. This review evaluates the evidence on whether individuals with diabetes would benefit from ERAS implementation. Methods MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL) and EMBASE searched with no language restrictions applied. Conference proceedings and bibliographies were reviewed. Experts in the field were contacted, and www.clinicaltrials.gov searched for ongoing trials. Selection criteria Randomized controlled trials (RCT) looking at individuals with diabetes undergoing surgery randomized to ERAS® or conventional care. Non‐randomized controlled trials, controlled before–after studies, interrupted time series, and cohort studies with concurrent controls were also considered. Two authors independently screened studies. Results The electronic search yielded 437 references. After removing duplicates, 376 were screened for eligibility. Conference proceedings and bibliographies identified additional references. Searching www.clinicaltrials.gov yielded 59 references. Contacting experts in the field identified no further studies. Fourteen full articles were assessed and subsequently excluded for the following reasons: used an intervention other than ERAS®, did not include patients with diabetes, or used an uncontrolled observational design. Conclusions To date, the effects of ERAS® on patients with diabetes have not been rigorously evaluated. This review highlights the lack of evidence in this area and provides guidance on design for future studies.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".