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Record W1738042447 · doi:10.1111/iwj.12325

A laboratory comparison between two liquid skin barrier products

2014· article· en· W1738042447 on OpenAlexaff
Kevin Woo, Debashish Chakravarthy

Bibliographic record

VenueInternational Wound Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsQueen's University
Fundersnot available
KeywordsTransepidermal water lossCyanoacrylateAbrasion (mechanical)MedicineMoistureSkin barrierDermatologySurgeryComposite materialStratum corneumMaterials scienceAdhesivePathology

Abstract

fetched live from OpenAlex

Exposure of skin to friction and moisture is detrimental to skin health. The purpose of this experimental study was to investigate the ability of a cyanoacrylate polymer film to protect human skin against moisture and abrasion. A secondary purpose of this study was to compare this cyanoacrylate material to a traditional barrier film. Twelve healthy subjects participated in the wash-off resistance test to determine the percentage of dye that was left on the skin after repeated washing. Ten subjects participated in the abrasion test. Transepidermal water loss (TEWL) was measured before and after abrasion to determine the level of skin damage, as high water loss seen post-abrasion is indicative of skin damage post-abrasion. Skin treated with cyanoacrylate had significantly more dye remaining than sites treated with traditional film barrier or control sites. The change in TEWL was statistically lower for cyanoacrylate-treated areas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.321
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2014
Admission routes1
Has abstractyes

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