The clinical and economic outcomes of an integrated care bundle using a three-layer silicone adhesive foam dressing for exudate management of chronic wounds: a retrospective cohort analysis
Bibliographic record
Abstract
IntroductionThe aim of this retrospective, real world, cohort analysis is to report the clinical and economic outcomes of an Integrated Care Bundle (ICB) that used a 3-layer silicone adhesive foam dressing for exudate management across multiple chronic wound types within a community setting in Canada.Methods Analysis of the safety and effectiveness of the introduction of wound centered ICBs, adopted to improve the management of chronic wounds, from March 2016 to December 2018.Outcomes were compared between patients who received a 3-layer silicone foam adhesive dressing alongside an ICB and those that did not, as part of their care. ResultsPatients who received care with an ICB and the dressing (n=6612) experienced improved clinical outcomes, compared with those who did not (n=2242).Including faster time to healing (12.7 vs 25.4 weeks, respectively) and longer time between dressings changes (3.5 vs 1.8 days, respectively).There were reduced number of nursing visits in the ICB cohort which led directly to reduced resource costs, compared to the patients in the non-ICB cohort (CAD$1736 vs $6488, respectively). ConclusionsThis real-world cohort analysis demonstrated the adoption of an ICB that included treatment with a three -layer silicone adhesive foam dressing improved clinical outcomes, reducing chronic wound healing times and the frequency of wound dressing changes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".