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Record W4400055041 · doi:10.7748/nop.2024.e1462

Assessment, prevention and management of skin tears in older people

2024· article· en· W4400055041 on OpenAlex
Kimberly LeBlanc, Karen Ousey

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

VenueNursing Older People · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsCanadian Nurses Association
Fundersnot available
KeywordsTearsOlder peopleMedicineGerontologyPsychologySurgery

Abstract

fetched live from OpenAlex

Skin tears are common injuries that result from mechanical forces. Older people with fragile skin are at greater risk of this type of wound. They are usually categorised as acute wounds that typically heal in 7-21 days but the healing process can be disrupted, leading to chronic, non-healing wounds. They have the potential to compromise quality of life and disrupt daily activities, so it is important to identify risk factors and implement prevention strategies for those at risk. An interdisciplinary approach has a pivotal role in promptly and precisely identifying skin tears, and the use of evidence-based interventions for efficient skin damage management can enhance the recovery process. This article adopts a case study approach to explore the prevention, evaluation and treatment of skin tears, using the case of an individual living with a skin tear in a community setting.

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 score0.810
Threshold uncertainty score0.906

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)0.0010.000

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 head0.026
GPT teacher head0.428
Teacher spread0.402 · 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