Prevention of Infections in Cardiac Surgery (PICS)-Prevena Study – A pilot/vanguard factorial cluster cross-over RCT
Bibliographic record
Abstract
Sternal surgical site infections after cardiac surgery can lead to significant morbidity, mortality, and cost. The effects of negative pressure wound management and adding vancomycin as perioperative antimicrobial prophylaxis are unknown. The PICS-PREVENA pilot/vanguard trial, a 2x2 factorial, open label, cluster-randomized crossover trial with 4 periods, was conducted at two major cardiac surgery hospitals in Ontario, Canada. Sites were randomized to one of eight sequences of the four study arms (Cefazolin or Cefazolin + Vancomycin (not analyzed) and standard wound dressing or a negative pressure 3M Prevena incision management system (Prevena). Only diabetic or obese patients were eligible for the latter comparison. This trial investigated feasability including adherence to protocol of each intervention (goal: > 90% each) and loss to follow-up (goal: < 10%). Among the 4107 included patients, 2230 were obese/diabetic (1208 standard wound dressing period, 1022 during Prevena period). Compliance to wound management and antimicrobial prophylaxis was 68.1% and 98.7%, respectively. Loss to follow-up was 3.6%. Deep/organ-space sternal surgical site infections occurred in 16 (1.6%) patients in the Prevena allocated periods and in 17 (1.4%) patients in the standard wound dressing allocated periods (OR= 1.11, 95% CI: 0.56-2.20). Other clinical outcomes did not suggest a difference and a post-hoc as-treated analysis showed similar results. This study showed challenges with introducing a novel technology as standard of care, with non-compliance mostly driven by one of the sites. No firm conclusions should be drawn regarding the effectiveness of Prevena, as this vanguard trial was not powered for clinical outcomes.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".