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Skin tears: prevention and management

2019· article· en· W2972263480 on OpenAlexaff
Kim LeBlanc, Diane Langemo, Kevin Woo, Heidi M. Hevia Campos, Vera Lúcia Conceição de Gouveia Santos, Samantha Holloway

Bibliographic record

VenueBritish Journal of Community Nursing · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsWest Park Healthcare CentreQueen's University
Fundersnot available
KeywordsMedicineTearsSkin careIncidence (geometry)Intensive care medicineSurgeryDermatology

Abstract

fetched live from OpenAlex

Skin tears commonly occur at the extremes of age and are associated with skin changes in ageing skin. They are considered to be acute wounds caused by mechanical forces, such as blunt trauma. While the true prevalence and incidence of skin tears is unknown, the available evidence suggests that these wounds occur in all healthcare settings. Importantly, skin tears are preventable with the implementation of a risk-reduction programme, which includes twice-daily skin moisturisation. Where these injuries do occur, it is important to categorise the extent of damage as well as to choose the most appropriate dressing to avoid adding any risks for further trauma.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.405
Teacher spread0.363 · 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
GenreEmpirical

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

Citations22
Published2019
Admission routes1
Has abstractyes

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