TITLE: Surgical Dressing for Patients Undergoing Hip or Knee Arthroplasty: Clinical Effectiveness and Cost-Effectiveness
Bibliographic record
Abstract
In fiscal year 2008-9, more than 64,000 hip and knee replacements and revisions were performed in Canada, representing almost 9 % of all surgical hospital discharges in that period. 1 The choice of dressing that is applied to the surgical wounds that result from these procedures may have an important impact on wound healing, through the prevention of blistering, maceration, and infection. 2 Given the large number of hip and knee arthroplasties performed in Canada on an annual basis, the choice of dressing could impact health outcomes as well as healthcare costs. While the cost of dressings may vary greatly, these costs should be evaluated in relation to dressing effectiveness as well – if some dressings require less frequent replacement or lead to fewer complications, their use may reduce other healthcare costs. A variety of surgical wound dressings are currently available including gauze, foam, bead, alginate, antimicrobial, semi-permeable films, hydrocolloid, and hydrogel. 3,4 The present review was undertaken to explore and summarize the evidence for the clinical and cost-effectiveness of surgical wound dressings used in hip or knee arthroplasty, with the aim of informing decisions to optimize clinical practice. RESEARCH QUESTIONS
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".