Routine Sterile Glove and Instrument Change at the Time of Abdominal Wound Closure to Prevent Surgical Site Infection: Reviewing the ChEETAh Trial
Bibliographic record
Abstract
Summary: “Evidence-Based Reviews in Surgery” (EBRS) was developed to foster critical appraisal skills in practicing surgeons and trainees in order that they may evaluate surgical literature and practice Evidence-based Based Surgery. EBRS virtually connects experts in clinical surgery and evidence-based methodology to collaboratively assess the strengths and weaknesses of current practice compared to the risks and benefits of new approaches to care. Since the inception of EBRS, summaries of each review have been published. The present article is a comprehensive review of the ChEETAh trial, investigating the effectiveness of routine glove and instrument change before abdominal wound closure to prevent surgical site infection (SSI). The trial was conducted in low- and middle-income countries (LMICs) and employed a cluster-randomized design. The results indicate a statistically significant reduction in SSI rates. Although the ChEETAh trial has strengths, caution is advised before implementing the intervention globally. The study's clinical relevance and cost-effectiveness need to be considered, and targeted implementation in specific patient clusters and hospitals with the necessary resources is recommended. Institutions should not only assess their unique circumstances (resources, baseline SSI rates, and use of other effective preventive measures) before implementing glove and instrument changes in their setting but also monitor their outcomes and costs should they choose in favor of implementation. Further research, including long-term effects, is suggested to refine the understanding of the intervention's implications in diverse settings.
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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 | 0.077 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".