Identifying Critical Evidence Gaps in Wound Closure and Incision Management After Total Hip Arthroplasty: Delphi Panel Insights
Bibliographic record
Abstract
Background: In total hip arthroplasty (THA), proper management of surgical incisions is essential for optimal wound healing and patient outcomes. Despite advances in surgical techniques, significant challenges remain in preventing complications and infections. This study aimed to identify evidence gaps in THA wound care, including presurgical preparation, intraoperative practices, and postsurgical complications. Methods: Using a modified Delphi method, 20 expert orthopedic surgeons from Europe and North America participated in a 3-phase consensus process from April 1 to September 30, 2023. This included a preliminary questionnaire, a remote conference, and a final online survey. The panel reviewed literature and achieved agreement on 18 consensus statements regarding wound care in THA. A secondary aim was to identify critical gaps in current wound care knowledge. Results: Consensus was reached on 18 statements. Key gaps were identified in the effectiveness of mesh-adhesive dressings, optimal closure methods (skin adhesives, staples, sutures), cost benefit of barbed sutures, and appropriate use of negative pressure wound therapy. These findings highlight the need for further research to validate best practices and guide standardized evidence-based protocols. Conclusions: Addressing these evidence gaps is essential to improve THA wound care methods. Future studies should compare closure techniques and new technologies to develop more efficient patient-centered strategies. Bridging these gaps may reduce complications, enhance outcomes, and lower the burden of wound-related issues in THA.
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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 | 0.284 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".