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In Vitro Characterization of Pressure Redistribution Among Commercially Available Wound Dressings

2020· article· en· W3108657700 on OpenAlexaboutno aff
Jeffrey Niezgoda, Jonathan Niezgoda, Sandeep Gopalakrishnan

Bibliographic record

VenueAdvances in Skin & Wound Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSiliconeSurgeryComposite materialMaterials science

Abstract

fetched live from OpenAlex

OBJECTIVE: Recent clinical evidence has suggested that certain wound dressings may play a significant role in protocols to prevent or reduce pressure injury (PI) in patients at risk by modifying the pressure, friction, and shear forces that can contribute to PI. The aim of this study was to investigate the pressure reduction properties of commercially available wound dressings in vitro. METHODS: Using a standardized protocol (1.7 kg, 7.5-cm sphere), testing was performed in a controlled environment by the same clinician using a pressure mapping device (XSENSOR LX205; XSENSOR Technology Corporation, Calgary, Alberta, Canada) to measure and compare the pressure mitigation properties in a variety of wound dressings. RESULTS: A total of 13 different commercially available dressings were tested in triplicate for changes in pressure redistribution as compared with the control. One dressing demonstrated the greatest reduction of pressure forces (OxyBand PR; 50.33 ± 1.45 mm Hg) compared with the control (302.7 ± 0.33 mm Hg) and the greatest surface area of all the study dressings tested. There was a negative correlation (R2 = 0.73) between the average pressure distribution of a wound dressing and its contact area. Further, the peak pressure for OxyBand PR (P ≤ .05) was significantly different from all other tested dressings. CONCLUSIONS: One dressing (OxyBand PR) provided superior pressure redistribution and significantly reduced peak pressure in this study when compared with currently available standard foam and silicone dressings that are marketed for the purpose of PI prevention.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.687

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 communication0.0000.001
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 head0.021
GPT teacher head0.341
Teacher spread0.321 · 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 designNot applicable
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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

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