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Record W4256246899 · doi:10.12968/joan.2019.8.1.27

Scars: impact and management, with a focus on topical silicone-based treatments

2019· article· en· W4256246899 on OpenAlexaff
Julie Van Onselen

Bibliographic record

VenueJournal of Aesthetic Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsLearning Partnership
Fundersnot available
KeywordsScarsMedicineMassageSiliconeWound careQuality of life (healthcare)Intensive care medicineNursingSurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Many people are living with scars caused by surgery or traumatic injury. Scar prevention in the early stages of wound healing is an essential aspect of care. Ongoing scar management is generally by self-care, using non-invasive methods such as silicone sheeting and/or massage. Silicone is considered to be the first-line non-invasive, prophylactic and therapeutic measure for scar management. Nurses have a role in supporting patients with self-care of their scars as well as providing psychological support, as the impact of a scar and effect of quality of life can be significant.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.334
Teacher spread0.320 · 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 designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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