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Record W2759110622

Comparison of efficacy of intralesional 5-fluorouracil plus triamcinolone acetonide versus intralesional triamcinolone acetonide in the treatment of keloids

2017· article· en· W2759110622 on OpenAlexaboutno aff
Farah Saleem, Zahida Rani, Bushra Bashir, Faria Altaf, Khawar Khurshid, Sabrina Suhail Pal

Bibliographic record

VenueJournal of Pakistan Association of Dermatology · 2017
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriamcinolone acetonideKeloidAcetonideSurgeryAdverse effectCorticosteroidFluorouracilDermatologyInternal medicineChemotherapy
DOInot available

Abstract

fetched live from OpenAlex

Objective To compare efficacy of intralesional 5-fluorouracil (5-FU) plus triamcinolone acetonide (TCA) versus intralesional TCA alone in the treatment of keloids. Methods The study included 100 patients with keloids. Patients were divided into two groups. Randomization was done through lottery method. For each 1 cm area, group A was given intralesional 5-FU 50 mg/ml (0.9ml) plus TCA 40mg/ml (0.1ml) after every 4 weeks and group B was given intralesional TCA 40mg/ml (0.1ml) after every 4 weeks for total period of 12 weeks. Administration of the drugs was continued till the keloid flattened or for a maximum period of 12 weeks. Follow-up was done every 4 weeks for total period of 12 weeks after the administration of last injection. Decrease in total score using Vancouver Scar Scale was calculated. Results After the completion of study mean reduction in Vancouver Scar Score was -71.18 ± 8.69 in the intralesional 5-FU plus TCA group as compared to -50.80 ± 8.59 in the intralesional TCA group (p=0.001). 5-FU + TCA was efficacious in 98% of cases (group A) and TCA alone in 62% of cases (group B). No serious adverse effects were noticed in either group. Conclusion Intralesional 5-FU plus TCA is significantly better than intralesional TCA alone in the treatment of keloids.

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)0.001
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 score0.055
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.093
GPT teacher head0.450
Teacher spread0.357 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

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