Sub-epidermal moisture assessment as an adjunct to visual assessment in the reduction of pressure ulcer incidence
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of sub-epidermal moisture (SEM) assessment technology as an adjunct to visual assessment to reduce pressure ulcer (PU) incidence alongside standard PU care pathways. METHOD: Data were obtained from wards located within 28 institutions in the UK, Canada, Belgium, Spain and Ireland. At each ward, the proportion of patients scanned who were observed to have one or more PUs of Category 2 or above during a pre-Pressure Ulcer Reduction Programme (PURP) implementation period starting between November 2017 and July 2018 was recorded. The proportion of patients scanned who were observed to have one or more PUs of Category 2 or above during a post-PURP implementation period starting between November 2018 and July 2019 was also recorded. A meta-analysis was conducted on the data using wards as the unit of analysis, to facilitate overall estimate of the PURP. A sensitivity study was also conducted to assess the sensitivity of results to data from specific institutions. RESULTS: A synthesised estimate of the overall relative risk (RR) was calculated to be 0.38 (95% confidence interval 0.26 to 0.56). Hence the risk of PU in the post-PURP cohort was about one-third that of the corresponding risk in the pre-PURP cohort. The sensitivity analysis revealed no evidence that any individual ward exerted excessive influence on the findings. CONCLUSION: The analysis has revealed strong evidence that implementation of the PURP was associated with reduction in incidence of Category 2 or above PUs across a wide range of clinical settings.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".