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Record W3207977087 · doi:10.1089/wound.2021.0060

Molecular Features of Hypertrophic Scars After Thermal Injury: Is There a Biologic Basis for Laser Therapy?

2021· review· en· W3207977087 on OpenAlexaff
Lindy Schaffrick, Jie Ding, Peter Kwan, Edward E. Tredget

Bibliographic record

VenueAdvances in Wound Care · 2021
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScarsMedicineReticular DermisHypertrophic scarsDermisHypertrophic scarWound healingLaser treatmentDermatologySurgeryPathologyLaser

Abstract

fetched live from OpenAlex

Significance: Hypertrophic scars (HTS) and keloids are common after thermal injuries and other trauma to deep regions of dermis of the skin. These abnormal scars can cause contractures and the thick masses of scar tissue that result in functional and cosmetic impairment. Management of these dermal fibrotic conditions includes a wide range of medical and surgical treatments, which can be time consuming, only partially effective, and often uncomfortable for patients.

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)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.027
GPT teacher head0.382
Teacher spread0.354 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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