Bibliographic record
Abstract
BACKGROUND: Pressure ulcers are common in many care settings, with adverse health outcomes and high treatment costs. We evaluated the cost-effectiveness of evidence-based strategies to improve current prevention practice in long-term care facilities. METHODS: We used a validated Markov model to compare current prevention practice with the following 4 quality improvement strategies: (1) pressure redistribution mattresses for all residents, (2) oral nutritional supplements for high-risk residents with recent weight loss, (3) skin emollients for high-risk residents with dry skin, and (4) foam cleansing for high-risk residents requiring incontinence care. Primary outcomes included lifetime risk of stage 2 to 4 pressure ulcers, quality-adjusted life-years (QALYs), and lifetime costs, calculated according to a single health care payer's perspective and expressed in 2009 Canadian dollars (Can$1 = US$0.84). RESULTS: Strategies cost on average $11.66 per resident per week. They reduced lifetime risk; the associated number needed to treat was 45 (strategy 1), 63 (strategy 4), 158 (strategy 3), and 333 (strategy 2). Strategy 1 and 4 minimally improved QALYs and reduced the mean lifetime cost by $115 and $179 per resident, respectively. The cost per QALY gained was approximately $78 000 for strategy 3 and $7.8 million for strategy 2. If decision makers are willing to pay up to $50 000 for 1 QALY gained, the probability that improving prevention is cost-effective is 94% (strategy 4), 82% (strategy 1), 43% (strategy 3), and 1% (strategy 2). CONCLUSIONS: The clinical and economic evidence supports pressure redistribution mattresses for all long-term care residents. Improving prevention with perineal foam cleansers and dry skin emollients appears to be cost-effective, but firm conclusions are limited by the available clinical 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".