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Record W1984186835 · doi:10.12968/bjon.2012.21.9.517

Moisture-associated skin damage: aetiology, prevention and treatment

2012· article· en· W1984186835 on OpenAlexaff
David Voegeli

Bibliographic record

VenueBritish Journal of Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsSKiN Health
Fundersnot available
KeywordsPerspirationPsychological interventionMedicineEtiologyNursing Interventions ClassificationIntensive care medicineSkin careMoistureDry skinDermatologyNursingPathology

Abstract

fetched live from OpenAlex

The concept of excessive moisture causing damage to the skin is not a new one, and provides a rationale for many fundamental nursing interventions. Although traditionally thought of as a specific problem of continence care, it is a common problem encountered in many different patient groups. As a consequence the umbrella term moisture-associated skin damage (MASD) has been introduced to describe the spectrum of damage that occurs in response to the prolonged exposure of a patient's skin to perspiration, urine, faeces or wound exudate. It is generally accepted that MASD consists of four main separate conditions, each having slightly different aetiologies, all of which will be explored in this paper. Careful assessment can help distinguish between the four and enable appropriate prevention and treatment interventions to be implemented. Whatever causes the excessive moisture, effective interventions should consist of the adoption of a structured skin care regime to cleanse and protect, methods to keep the skin dry, controlling the source of the excessive moisture and treating any secondary infection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.070
GPT teacher head0.425
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations64
Published2012
Admission routes1
Has abstractyes

Explore more

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