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Record W4391814110 · doi:10.1111/iwj.14674

Fluid handling by foam wound dressings: From engineering theory to advanced laboratory performance evaluations

2024· review· en· W4391814110 on OpenAlexaff
Amit Gefen, Paulo Alves, Dimitri Beeckman, Breda Cullen, José Luis Lázaro‐Martínez, Hadar Lev‐Tov, Nick Santamaria, Terry Swanson, Kevin Woo, Bengt Söderström, Anna U. Svensby, Matthew Malone, Erik Nygren

Bibliographic record

VenueInternational Wound Journal · 2024
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineWound dressingBiomedical engineeringComposite material

Abstract

fetched live from OpenAlex

This article describes the contemporary bioengineering theory and practice of evaluating the fluid handling performance of foam-based dressings, with focus on the important and clinically relevant engineering structure-function relationships and on advanced laboratory testing methods for pre-clinical quantitative assessments of this common type of wound dressings. The effects of key wound dressing material-related and treatment-related physical factors on the absorbency and overall fluid handling of foam-based dressings are thoroughly and quantitively analysed. Discussions include exudate viscosity and temperature, action of mechanical forces and the dressing microstructure and associated interactions. Based on this comprehensive review, we propose a newly developed testing method, experimental metrics and clinical benchmarks that are clinically relevant and can set the standard for robust fluid handling performance evaluations. The purpose of this evaluative framework is to translate the physical characteristics and performance determinants of a foam dressing into achievable best clinical outcomes. These guiding principles are key to distinguishing desirable properties of a dressing that contribute to optimal performance in clinical settings.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.032
GPT teacher head0.391
Teacher spread0.359 · 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 designOther design
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

Citations34
Published2024
Admission routes1
Has abstractyes

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