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Record W3185339057 · doi:10.1055/s-0041-1731461

Current Advances in Hypertrophic Scar and Keloid Management

2021· review· en· W3185339057 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSeminars in Plastic Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineKeloidHypertrophic scarHypertrophic scarsScarsWound healingEtiologyDermatologyPathologySurgery

Abstract

fetched live from OpenAlex

Hypertrophic scars and keloids are caused by excessive tissue response to dermal injury due to local fibroblast proliferation and collagen overproduction. This response occurs because of pathologic wound healing due to dysregulation in the inflammatory, proliferative, and/or remodeling phase. Patients with hypertrophic scars or keloids report reduced quality of life, physical status, and psychological health. Hypertrophic scars or keloids will develop in 30 to 90% of individuals, and despite their prevalence, treatment remains a challenge. Of the treatments currently available for hypertrophic scars and keloids few have been adequately supported by studies with appropriate experimental design. Here, we aim to review the available literature to provide up-to-date information on the etiology, epidemiology, histology, pathophysiology, prevention, and management options available for the treatment of hypertrophic scars and keloids and highlight areas where further research is required.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.368
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