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Record W2193068549 · doi:10.1097/won.0000000000000195

Is Twice-Daily Skin Moisturizing More Effective Than Routine Care in the Prevention of Skin Tears in the Elderly Population?

2015· review· en· W2193068549 on OpenAlexaff
Kimberly LeBlanc, Kathryn Kozell, Lina Martins, Louise Forest-Lalande, Marilyn Langlois, Mary Hill

Bibliographic record

VenueJournal of Wound Ostomy and Continence Nursing · 2015
Typereview
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsProfessional Engineers OntarioAlberta Health ServicesGovernment of CanadaQueen's UniversityWestern University
Fundersnot available
KeywordsMoisturizerMedicineSkin careTearsDermatologyIncidence (geometry)Dry skinSurgery

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.444
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2015
Admission routes1
Has abstractyes

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