Prescription Pattern of Tofacitinib for Alopecia Areata Among the Dermatologists in Saudi Arabia: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction Alopecia areata (AA) is a complex autoimmune condition that causes nonscarring hair loss. In Saudi Arabia, AA accounts for 1-2% of new dermatological outpatient visits. It typically presents with sharply demarcated round patches of hair loss and may present at any age. Traditional medical therapies include corticosteroids and immunotherapy. Choosing the ideal treatment depends on multiple factors such as patient age, disease severity, efficacy, side effects, and remission rate. Recent medications that have been used for treating AA are Janus kinase inhibitors. Aim The aim of the study is to assess the awareness and attitude of dermatologists and their use of Tofacitinib in treating AA. Method A cross-sectional study was conducted in 2019 across 14 major cities in Saudi Arabia. A self-administered online questionnaire was specifically developed and used. Dermatologists from government hospitals and private clinics were included through non-probability convenience sampling. The collected data was entered into Microsoft Excel and analyzed using SPSS program version 24. Results In total, out of 546 Dermatologists across Saudi Arabia who responded to the questionnaire, 127 (23.2%) physicians prescribed Tofacitinib in their practice. Out of those who prescribed the drug for AA cases, 58 dermatologists (45.6%) prescribed Tofacitinib after the failure of steroid injections. Among the 127 dermatologists who have utilized Tofacitinib in their practice, 92 (72.4%) believe that Tofacitinib is effective in treating AA. Almost 200 (47.7%) Dermatologists who never prescribed Tofacitinib reported that the main reason was due to the unavailability of the drug in the clinic they were practicing. Conclusions To conclude, out of 546 dermatologists working in Saudi Arabia, 127 (23.2%) prescribe Tofacitinib to treat AA. Ninety-two (72.4%) of the participants reported the effectiveness of Tofacitinib. Two hundred (47.7%) dermatologists who never prescribe Tofacitinib reported that the main reason was due to the unavailability. However, this would raise the need for more research regarding JAK inhibitors generally and Tofacitinib specifically, focusing on the effectiveness versus the side effects of Tofacitinib.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".