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Record W4284685628 · doi:10.12968/jowc.2022.31.7.579

The prevalence of skin tears and associated factors in hospitalised patients with cancer

2022· article· en· W4284685628 on OpenAlexaff
Mariana Alves Bandeira, Fernanda Mateus Queiróz Schmidt, Paula Cristina Nogueira, Talita dos Santos Rosa, Roberto de Miranda Felizardo, Diana Lima Villela de Castro, Kevin Woo, Vera Lúcia Conceição de Gouveia Santos

Bibliographic record

VenueJournal of Wound Care · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineObservational studyEpidemiologyCross-sectional studyPsychological interventionSkin cancerCancerPhysical therapyInternal medicineNursingPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Oncology patients are vulnerable to skin breakdown. The primary purpose of this study was to estimate the prevalence of skin tears (STs) in hospitalised patients with cancer and to explore related sociodemographic and clinical factors. METHOD: This was an observational, epidemiological, cross-sectional study conducted in an oncology hospital in the city of São Paulo. All STs were classified using the STAR Classification adapted and validated for Brazil. RESULTS: Of the 341 patients evaluated, 22 had STs, equating to a prevalence of 6.5%. A higher number of STs were noted on the lower limbs (26.9%) than on other body areas. The main factors associated with STs were the use of anticoagulants, the presence of ecchymosis and the use of incontinence briefs. CONCLUSION: This study contributed to a better understanding of the epidemiology of STs in hospitalised patients with cancer, as well as its associated factors. Results may inform nursing professionals with regard to the need to develop prevention strategies and early interventions.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.326
Teacher spread0.313 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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