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Record W4403938792 · doi:10.12968/jowc.2024.0027

Using patient-reported experiences to inform the use of foam dressings for hard-to-heal wounds: perspectives from a wound care expert panel

2024· review· en· W4403938792 on OpenAlexaff
Kevin Woo, Nick Santamaria, Dimitri Beeckman, Paulo Alves, Breda Cullen, Amit Gefen, José Luis Lázaro‐Martínez, Hadar Lev‐Tov, Bijan Najafi, Andrew Sharpe, Terry Swanson

Bibliographic record

VenueJournal of Wound Care · 2024
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsQueen's UniversityToronto Public Health
Fundersnot available
KeywordsWound careMedicinePsychosocialNursingHealth careAccountabilityIntensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

Caring for patients with hard-to-heal (chronic) wounds requires a multifaceted approach that addresses their diverse needs, which can contribute to the complexity of care. Wound care providers must have a comprehensive understanding of the patient's comorbid conditions and psychosocial issues to provide personalised and effective treatment. Key quality indicators for effective wound care involves not only selecting appropriate local wound care products, such as foam dressings, but also addressing individual patient experiences of wound-related pain, odour, itch, excessive wound drainage, and self-care needs. The purpose of this review is to inculcate the wound care practice community, research scientists and healthcare industry with a sense of accountability in order to work collaboratively in addressing these unmet care needs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.321
GPT teacher head0.444
Teacher spread0.123 · 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
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

Citations10
Published2024
Admission routes1
Has abstractyes

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