International Delphi Study on Wound Closure and Dressing Management in Joint Arthroplasty: Part 1: Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: The purpose of this modified Delphi study was to obtain consensus on wound closure and dressing management in total knee arthroplasty (TKA). METHODS: The Delphi panel included 20 orthopaedic surgeons from Europe and North America. There were 26 statements identified using a targeted literature review. Consensus was developed for the statements with up to three rounds of anonymous voting per topic. Panelists ranked their agreement with each statement on a five-point Likert scale. An a priori threshold of ≥ 75% was required for consensus. RESULTS: All 26 statements achieved consensus after three rounds of anonymous voting. Wound closure-related interventions that were recommended for use in TKA included: 1) closing in semi-flexion versus extension (superior range of motion); 2) using aspirin for venous thromboembolism prophylaxis over other agents (reduces wound complications); 3) barbed sutures over non-barbed sutures (lower wound complications, better cosmetic appearances, shorter closing times, and overall cost savings); 4) mesh-adhesives over other skin closure methods (lower wound complications, higher patient satisfaction scores, lower rates of readmission); 5) silver-impregnated dressings over standard dressings (lower wound complications, decreased infections, fewer dressing changes); 6) in high-risk patients, negative pressure wound therapy over other dressings (lower wound complications, decreased reoperations, fewer dressing changes); and 7) using triclosan-coated over non-antimicrobial-coated sutures (lower risks of surgical site infection). CONCLUSIONS: Using a modified Delphi approach, the panel achieved consensus on 26 statements pertaining to wound closure and dressing management in TKA. This study forms the basis for identifying critical evidence supported by clinical practice for wound management to help reduce variability, advance standardization, and ultimately improve outcomes during TKA. The results presented here can serve as the foundation for knowledge, education, and improved clinical outcomes for surgeons performing TKAs.
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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 | 0.095 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".