Is Twice-Daily Skin Moisturizing More Effective Than Routine Care in the Prevention of Skin Tears in the Elderly Population?
Bibliographic record
Abstract
BACKGROUND: Skin-moisturizing routines are part of a bundle of interventions designed to prevent skin tears. OBJECTIVE: This Evidence-Based Report Card reviews the effect of twice-daily moisturization of the skin on skin tear occurrence versus occurrence rates using routine skin care. SEARCH STRATEGY: The literature was systematically reviewed for studies that evaluated the use of standardized skin moisturizers on the rate of skin tears in the older adults (>60 years of age). A professional librarian performed the literature search, which yielded 446 articles. Following title and abstract reviews, we identified and retrieved 3 studies that met inclusion criteria. FINDINGS: Evidence concerning the effectiveness of routine twice-daily skin moisturizing reducing the rate of skin tears is mixed. Routine twice-daily skin moisturizing did not significantly result in a lower incidence of skin tears in long-term care residents compared to usual care in one study. However, the occurrence of skin tears per 1000 occupied beds was 50% lower when a moisturizer applied twice daily was compared to usual care. CONCLUSION: Routine skin moisturizing is recommended as one component of a prevention program for skin tears among aged adults residing in long-term care facilities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".