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Treatment of keloids and hypertrophic scars using topical and intralesional mitomycin C

2011· article· en· W2156752733 on OpenAlexaboutno aff
Sang‐Hee Seo, Hyeon-Me Sung

Bibliographic record

VenueJournal of the European Academy of Dermatology and Venereology · 2011
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMitomycin CHypertrophic scarsDermatologyScarsSurgeryKeloidAdverse effectHypertrophic scarInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Keloids develop due to the overgrowth of fibrous tissue. Currently, there is no gold standard treatment for keloids and hypertrophic scars (HTS). Their propensity for local invasion and recurrence has prompted many investigations on antineoplastic agents. OBJECTIVES: To investigate the efficacy of topical and intralesional mitomycin C for the treatment of keloids and HTS. METHODS: Nine patients with clinically diagnosed keloids and HTS were treated using topical mitomycin C (1 mg/mL) for 3 min after shaving excision. The Vancouver Scars Scale, patient satisfaction, and adverse effects were checked after 6 months. The keloids and HTS were photographed at each monthly visit. Intralesional mitomycin C (1 mg/mL) was administered to study the effect on the regression of keloids in 2 patients. RESULTS: Application of mitomycin C to the base of shave-removed keloids and HTS showed good results. Six out of 9 patients were very satisfied with the outcome of treatment; none were disappointed. The results of intralesional mitomycin C treatment were disappointing. Both cases worsened, with increased ulceration after treatment. CONCLUSIONS: Topical application of mitomycin C following shaving excision was safe and effective for the treatment of keloids and HTS. However, intralesional mitomycin C therapy aggravated both lesions.

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 score0.099
Threshold uncertainty score0.311

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 studies0.0000.001
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 head0.080
GPT teacher head0.317
Teacher spread0.238 · 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

Citations36
Published2011
Admission routes1
Has abstractyes

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