Efficacy of hydrofibre dressing following total joint arthroplasty: a meta-analysis of randomised controlled trials
Bibliographic record
Abstract
INTRODUCTION: In the United States, over 1,000,000 total joint arthroplasty (TJA) surgeries are performed annually and has been forecasted that this number will exceed 4,000,000 by the year 2030. Many different types of dressing exist for use in TJA surgery, and it is unclear if any of the newer, hydrofibre dressings are superior to traditional dressings at reducing rates of infections or improving wound healing. Thus, the aim of this systematic review and meta-analysis was to assess the impact of hydrofiber dressings on reducing complications. METHODS: A systematic review and meta-analysis was performed using the online databases MEDLINE and the Cochrane Library. Randomized controlled trials (RCTs) comparing hydrofibre dressings to a standard dressing were included. Summary measures are reported as odds ratios (ORs) and mean differences (MDs) with 95% confidence intervals (CIs). Our primary outcome was prosthetic joint infection (PJI). Secondary outcomes included blisters, dressing changes and wound irritation. RESULTS: = 0.03) and number of dressing changes (MD -1.89; 95% CI, -2.68 to -1.11). CONCLUSIONS: In conclusion, evidence suggests hydrofibre dressings have no observable effect on PJI and wound irritation. Evidence for reduction in blisters and number of dressings is modest given wide CIs and biased trial methodologies. Use of hydrofibre dressings should be considered inconclusive for mitigating major complications in light of current best evidence.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.065 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".