COST-EFFECTIVENESS OF TWO DRESSINGS IN PREVENTING PRESSURE INJURIES IN INTENSIVE CARE PATIENTS
Bibliographic record
Abstract
ABSTRACT Objective: to analyze the cost-effectiveness of using silicone adhesive multilayer foam and transparent polyurethane film in preventing pressure injuries in patients admitted to an Intensive Care Unit. Method: this is an economic cost-effectiveness study, developed with public domain data and a rapid literature review, which included three studies developed in Canada, the United Kingdom, Italy and the United States. The searches were carried out in the PubMed, Cochrane and Scopus databases. The population was patients admitted to the Intensive Care Unit. The perspective was from the Brazilian Health System, with a time horizon of less than one year. Data was collected and analyzed between March and June 2022. A decision tree model was developed using TreeAge Pro® 2017 software to project economic outcomes of incremental cost and effectiveness, incremental cost per effectiveness, and cost per percentage increase in preventing the development of pressure injuries. Sensitivity analysis was also performed. The Consolidated Health Economic Evaluation Reporting Standards and the Methodological Guideline for Economic Evaluation of the Brazilian Ministry of Health recommendations were adopted. Results: multilayer foam reduces the occurrence of pressure injuries at a lower cost when compared to film, promoting an average saving of R$ 278.78 (US$ 1,393.90) for each patient. Conclusion: multilayer foam was the most cost-effective technology in preventing pressure injuries in Intensive Care Unit patients in the Brazilian Health System.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".