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Record W3004393739 · doi:10.1093/jbcr/irz158

Hypertrophic Scarring: Current Knowledge of Predisposing Factors, Cellular and Molecular Mechanisms

2020· review· en· W3004393739 on OpenAlexafffund
Layla Nabai, Amir Pourghadiri, Aziz Ghahary

Bibliographic record

VenueJournal of Burn Care & Research · 2020
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchWorkSafeBC
KeywordsMedicineIntensive care medicinePathophysiologyMechanism (biology)BioinformaticsPathologyBiology

Abstract

fetched live from OpenAlex

Hypertrophic scarring (HSc) is an age-old problem that still affects millions of people physically, psychologically, and economically. Despite advances in surgical techniques and wound care, prevention and treatment of HSc remains a challenge. Elucidation of factors involved in the development of this common fibroproliferative disorder is crucial for further progress in preventive and/or therapeutic measures. Our knowledge about pathophysiology of HSc at the cellular and molecular level has grown considerably in recent decades. In this article, current knowledge of predisposing factors and the cellular and molecular mechanisms of HSc has been reviewed.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.135
GPT teacher head0.448
Teacher spread0.314 · 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

Citations40
Published2020
Admission routes2
Has abstractyes

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