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Record W2554036519

Clinician perspectives on medical adhesive-related skin injuries

2016· article· en· W2554036519 on OpenAlexaff
Karen Ousey, Stephanie Wasek

Bibliographic record

VenueHuddersfield Research Portal (University of Huddersfield) · 2016
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineIncidence (geometry)Patient educationWound careIntensive care medicineMedical emergencyNursing
DOInot available

Abstract

fetched live from OpenAlex

Medical adhesive-related skin injury (MARSI) is a prevalent, under-recognised and preventable complication that occurs across all care settings, age groups and patient types. Use of medical adhesives may affect skin integrity, cause pain, increase risk of infection, potentially increase wound size and delay healing, all of which reduce patient quality of life unnecessarily. In addition, MARSI is costly in terms of nursing time and costs. A new survey of UK wound care clinicians sought to understand clinician experiences of and perspectives on MARSI and found that incidence of MARSI is high, yet education around assessment of risk and prevention are low. The results of the survey show that clinicians both need and want improved educational efforts around MARSI awareness, identification of patients at risk of MARSI and strategies for preventing MARSI. Broadly, more research on the exact pathophysiology of MARSI is needed, in order to deepen understanding and aid the development of formal MARSI education programmes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5770.000

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 head0.348
Teacher spread0.301 · 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.

Study designQualitative
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

Citations23
Published2016
Admission routes1
Has abstractyes

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