A Retrospective Review of Anthralin in Petrolatum in the Treatment of Alopecia Areata in the Pediatric Population
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Alopecia areata (AA) is a T-cell driven autoimmune disease, which results in hair loss. This study aims to determine the efficacy, tolerability and safety of different concentrations of anthralin in the treatment of pediatric AA. METHODS: A retrospective cohort study of patients < 18 yo diagnosed with AA treated with anthralin at SickKids Hospital, Toronto dermatology outpatient clinic in 2016 - 2018. Anthralin used at 0.1%, 0.2%, 0.5% and 1% in petrolatum at short contact, at increments of 15 minutes every week until a 1 hr maximum contact achieved. No other treatment was used in conjunction. Severity of Alopecia Tool (SALT) scores (SS) were determined using photographs and descriptions to assess severity of alopecia at baseline and post anthralin treatment. RESULTS: A total of 11 charts were reviewed in this retrospective cohort. Hair loss pattern; 3 patients with patchy, 6 had mixed (patchy and ophiasis), and 2 were totalis. All except for 1 patient had failed traditional treatments. One patient had complete hair regrowth, 3 showed more than 85% hair re-growth and 7 patients showed more than 75% hair regrowth, the average time for this to occur was 6.5 months. None of the patients experience serious side effects. CONCLUSIONS: Our study demonstrated the efficacy and tolerability of topical anthralin 0.1% to 1% in pediatric alopecia areata. In our study, anthralin 0.2% appears to offer the best performance and tolerability profile among the different concentrations used, with treatment course of at least 6 months in order to achieve more than 75% hair regrowth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".