Measuring subepidermal moisture to detect early pressure ulcer development: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The aim was to assess evidence related to the measuring of subepidermal moisture (SEM) to detect early, nonvisible development of pressure ulcers (PUs). METHOD: Using systematic review methodology, all quantitative animal and human research studies written in English were considered. In January 2021, PubMed, CINAHL, SCOPUS, Cochrane and EMBASE databases were searched. The primary outcome of interest was the validity of SEM measurement to detect early PU development. The secondary outcome was time to PU detection, sensitivity and specificity of SEM measurement, and the impact of SEM measurements on PU prevention. Data analysis was undertaken using RevMan and narrative synthesis. RESULTS: A total of 17 articles met the inclusion criteria. In all studies, a consistent abnormal deviation in SEM measurements corresponded with evidence of visual PU development. Time to PU development, explored in four studies, showed earlier detection of PU development using SEM measurement. RevMan analysis identified the mean difference in time to PU development (SEM measurement versus visual skin assessment, VSA) was 4.61 days (95% confidence interval: 3.94-5.28; p=0.0001) in favour of SEM measurements. The sensitivity of SEM measurements was reported in four studies, and scores varied from 48.3% to 100.0%. Specificity was also reported in four studies and scores ranged from 24.4% to 83.0%. The impact of the detection of abnormal SEM measurements on PU prevention was explored by one study. Results showed a 93% decrease in PU rates when staff acted on the results of the SEM readings. CONCLUSION: The findings of this review identified that SEM measurement detects PU development earlier than VSA. Furthermore, when staff responded to abnormal SEM measurements, prevention strategies were enhanced, with a subsequent reduction in visible PU development. SEM measurement may therefore be a useful addition to PU prevention strategies. DECLARATION OF INTEREST: The School of Nursing & Midwifery, RCSI has a research agreement with Bruin Biometrics. Funding for the study was through an Irish Research Council PhD Enterprise Partnership Scheme with Bruin Biometrics. The authors have no other conflicts of interest.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".