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Record W1476610668 · doi:10.1016/j.jdds.2015.07.003

Comparative study between intralesional injection of bleomycin and 5-fluorouracil in the treatment of keloids and hypertrophic scars

2015· article· en· W1476610668 on OpenAlexaboutno aff
Ahmed M. Kabel, Hanan Hassan Sabry, N.E. Sorour, Fatma M. Moharm

Bibliographic record

VenueJournal of Dermatology & Dermatologic Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBleomycinScarsFluorouracilTriamcinolone acetonideSurgeryHyperpigmentationHypertrophic scarKeloidLesionHypertrophic scarsChemotherapyDermatology

Abstract

fetched live from OpenAlex

The aim of this work was to evaluate the efficacy and safety of intralesional injection of 5-fluorouracil and bleomycin in the treatment of keloids and hypertrophic scars . One hundred and twenty patients were divided into the following groups: group IA was injected intralesionally with 5-fluorouracil; group IB was injected intralesionally with a combination of triamcinolone acetonide and 5-fluorouracil; group II was injected intralesionally with bleomycin . Patients underwent follow up by photographing and Vancouver scar scale system. There was a significant improvement in the Vancouver scar scale in group II compared to group I after treatment. There was hyperpigmentation , pain and ulceration in all the studied groups. Pain was significantly decreased in group IB compared to that in group IA, ulceration was significantly decreased in group II than in group I while pain after injection was increased in group II than in group I. Relapse occurred in 12 patients of group IA, 14 patients of group IB and no relapse occurred in group II. So, intralesional injection of bleomycin was more effective and better in remission than intralesional 5-fluorouracil injection in the treatment of keloids and hypertrophic scars regardless of patient’s age, sex, disease duration or site of the lesion.

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)0.001
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 score0.113
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.127
GPT teacher head0.362
Teacher spread0.235 · 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

Citations29
Published2015
Admission routes1
Has abstractyes

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