Economic Evaluations of Strategies to Prevent Hospital-Acquired Pressure Injuries
Bibliographic record
Abstract
GENERAL PURPOSE: To provide information from a review of literature about economic evaluations of preventive strategies for pressure injuries (PIs). TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant should be better able to:1. Identify the purpose and methods used for this study.2. Compare costs and effectiveness related to preventative strategies for PIs. ABSTRACT: BACKGROUND: Pressure injuries (PIs) are a common and resource-intensive challenge for acute care hospitals worldwide. While a number of preventive strategies have the potential to reduce the cost of hospital-acquired PIs, it is unclear what approach is the most effective. OBJECTIVE: The authors performed a narrative review of the literature on economic evaluations of preventive strategies to survey current findings and identify important factors in economic assessments. DATA SOURCES: Ovid, MEDLINE, NHS Economic Evaluation Databases, and the Cochrane Database of Systematic ReviewsSELECTION CRITERIA: Potentially relevant original research articles and systematic reviews were considered. DATA EXTRACTION: Selection criteria included articles that were written in English, provided data on cost or economic evaluations of preventive strategies of PIs in acute care, and published between January 2004 and September 2015. Data were abstracted from the articles using a standardized approach to evaluate how the items on the Consolidated Health Economic Evaluation Reporting Standards checklist were addressed. DATA SYNTHESIS: The searches identified 192 references. Thirty-three original articles were chosen for full-text reviews. Nineteen of these articles provided clear descriptions of interventions, study methods, and outcomes considered. CONCLUSIONS: Limitations in the available literature prevent firm conclusions from being reached about the relative economic merits of the various approaches to the prevention of PIs. The authors' review revealed a need for additional high-quality studies that adhere to commonly used standards of both currently utilized and emerging ways to prevent hospital-acquired PIs.
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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 | 0.040 | 0.184 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".