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Record W4399576955 · doi:10.21649/akemu.v30i1.5148

Effective Treatment of Keloids with Three-Dose Moderate-Strength Intralesional Triamcinolone Acetonide (TAC) Regimen

2024· article· en· W4399576955 on OpenAlexaboutno aff
Tauqeer Nazim, Jumana Fatima, Muhammad Ali Shakir M. Mujahid Rafique, Saadia Nosheen Jan, Ayesha Usman, Sarfraz Ahmed

Bibliographic record

VenueAnnals of King Edward Medical University · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriamcinolone acetonideDermatologyRegimenKeloidAcetonideSurgery

Abstract

fetched live from OpenAlex

Background: The most devastating consequence of any injury is scar formation. Among all the surgical specialties, plastic surgery faces the worst dilemma as patients expect it to be the scar-less surgery.Objectives: This study aims to set an effective dose of triamcinolone acetonide intralesional injection to achieve successful results in the treatment of keloids.Methods: A prospective interventional study was conducted in the Department of Plastic Surgery, Shaikh Zayed Hospital Lahore for 2 years. Triamcinolone acetonide was injected intralesional at a dose of 4mg/cm2 every 4th week. The improvement in scar appearance, pain, and itch were measured using Vancouver Scar Scale (VSS), Visual Analog Scale (VAS), and the St Andrew's itch egg scale, respectively up to 12 months of therapy.Results: Among the 40 patients, 12 were males and 28 were females. The mean age of patients was 32.8 years and the most common sites were the chest, earlobes, and back. There was a substantial progressive improvement in VSS and VAS scores over one year. A significant reduction in pruritus was also observed in the patients. No recurrence was noted at the end of 12 months.Conclusions: A moderate-strength dose of 4mg/cm2 as a single intralesional injection of triamcinolone acetonide every four weeks is effective in decreasing the size of keloids and relieving the symptoms such as pain and itching.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.335
Teacher spread0.286 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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