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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
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