Clinical evaluation of adults undergoing elective surgery utilizing intraoperative incisional wound irrigation (CLEAN Wound): protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: In moderate to high-risk surgical procedures, 15-25% of patients develop a postoperative surgical site infection. Intraoperative incisional wound irrigation has the potential to reduce surgical site infections, and additional randomised controlled trials are required to provide evidence of effectiveness. METHODS AND ANALYSIS: This protocol describes a pragmatic, adaptive, participant and adjudicator-blinded trial at 13 sites in Canada in up to 2500 participants. Participants planned for surgery with an abdominal or groin incision, who are eligible and provide verbal consent through an integrated consent model, are randomised to receive intraoperative incisional wound irrigation with povidone-iodine, saline or no irrigation. The primary outcome is surgical site infection within 30 days postoperatively. Secondary outcomes include quality of life measured 30 days postoperatively and morbidity, mortality and healthcare utilisation within 90 days postoperatively. ETHICS AND DISSEMINATION: This trial has been approved by the research ethics board at the participating centres and stopped enrolling participants on May 23, 2025. All participants will provide verbal consent. Results will be disseminated via presentation at conferences, publication and posted on clinicaltrials.gov. TRIAL REGISTRATION NUMBER: The study is registered with http://clinicaltrial.gov (NCT04548661; 14 September 2020).
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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 | 0.052 | 0.059 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.091 | 0.019 |
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".