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Progress in scar prevention and treatment

2019· article· en· W3031682709 on OpenAlexaboutno aff
Xiao-peng Shen

Bibliographic record

VenueChin J Clinicians(Electronic Edition) · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsHypertrophic scarKeloidMedicineScar tissueSurgeryScarsWound healingRadiation therapyDermatology

Abstract

fetched live from OpenAlex

At present, scar is one of the key areas of medical research, and its evaluation, prevention, and control methods have received more and more attention. Scar is generally divided into superficial scar, atrophic scar, hypertrophic scar, and keloid. The mechanism is thought to involve abnormal proliferation of fibroblasts and imbalance of secretion, synthesis, and degradation of collagen caused by various factors during the skin wound healing process, which eventually result in the formation of abnormal hypertrophic skin tissue. The severity of scar is measured mainly using the Vancouver scar scale (VSS), the Patient and Observer scar Assessment scale (POSAS) and so on. Its main treatments include pressure therapy, silicone gel therapy, radiation therapy, laser therapy, intralesional corticosteroids, and surgical therapy. Now it is believed that combination therapy is still the safe and effective strategy for the prevention and control of scar. Key words: Scar; Hyperplastic; Keloid; Surgery; Combined therapy

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.359
Teacher spread0.340 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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