The Prospective Randomized EValuation of Emerging Novel Treatments for Infection Prophylaxis in Total Joint Replacement (PREVENT-iT)
Bibliographic record
Abstract
Background: Despite the success of total joint arthroplasty for end-stage hip and knee osteoarthritis, periprosthetic joint infection (PJI) remains a devastating complication and leading cause of revision surgery. Antiseptic irrigation solutions and topical antibiotics are promising and cost-effective strategies for the prevention of PJIs, though high-quality evidence assessing their efficacy is lacking. Therefore, this study investigates the feasibility of conducting a definitive trial to determine the optimal prophylactic treatment of PJIs using various irrigation solutions and topical antibiotics. Methods: Using a simple randomized 3 × 2 factorial trial, patients were randomized across 5 centers to 1 of 6 possible treatments (povidone-iodine, chlorhexidine-gluconate, or saline, with or without vancomycin). Nine criteria were assessed to evaluate feasibility including participant enrollment, administration of treatments, data collection methods, and protocol compliance. Adverse event rates were used to assess trial safety. Secondary outcomes included rates of PJI requiring reoperation and persistent wound drainage (PWD). Results: Four hundred and ninety-five participants were included in the pilot trial. Study participants were 56% female with a mean age of 67 years. Seven of the 9 criteria assessing feasibility indicated the trial was successful and no modifications needed. Two criteria, treatment contamination (8.5%) and completeness of patient follow-up (93.8%), were graded as requiring minor adjustment before conducting the definitive trial. There were 114 serious adverse events; none of which were deemed associated with the treatments. Overall, 9 (1.84%) presented with PJIs requiring reoperation, and 6 patients (1.12%) presented with PWD. Conclusion: This study demonstrates the feasibility and safety of prophylactic irrigation solutions and topical antibiotics. PREVENT-IT has received funding from the Canadian Institutes of Health Research toward the definitive, large, multicenter randomized controlled trial (NCT06126614).Ultimately, findings will directly affect clinical practice with the potential to positively influence global rates of PJI. Level of Evidence: NA.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".