E-Health Decision Support Technologies in the Prevention and Management of Pressure Ulcers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pressure ulcers are problematic across clinical settings, negatively impacting patient morbidity and mortality while resulting in substantial costs to the healthcare system. E-health clinical decision support technologies can play a key role in improving pressure ulcer-related outcomes. This systematic review aimed to assess the impact of electronic health decision support interventions on pressure ulcer management and prevention. A systematic search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, and Cochrane. Nineteen articles, published from 2010 to 2020, were included for review. The findings of this review showed promising results regarding the usability and accuracy of electronic health decision support tools to aid in pressure ulcer prevention and management. Evidence indicated improved clinician adherence to pressure ulcer prevention practices and decreased healthcare costs postimplementation of an electronic health decision support intervention. However, the studies included in this review did not consistently show reductions in pressure ulcer prevalence, incidence, or risk. Most of the articles included in the review were limited by small sample sizes drawn from single hospitals or long-term care homes. More high-quality studies are needed to determine the types of electronic health decision support tools that can drive sustainable improvements to patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it