Efficacy of tofacitinib in treatment of alopecia universalis in two patients
Bibliographic record
Abstract
BACKGROUND: Autoimmune-triggered non-scarring hair loss is a feature of alopecia areata (AA). Initially patchy and often self-limited, severe hair loss forms include the complete loss of scalp hair or alopecia totalis (AT) and complete loss of all hair or alopecia universalis (AU). For AT and AU a reliable treatment has remained elusive. The targeted kinase inhibitor tofacitinib, in current use for treatment of other immune diseases, has been hypothesized as a viable option for AA, AT and AU therapy and a few case reports support this. OBJECTIVE: Our study aims to provide evidence for the effectiveness of tofacitinib in the treatment of AU. METHODS: Two patients diagnosed with long-term AU were prescribed tofacitinib citrate at a dosage of 5 mg twice daily and observed for eight months. RESULTS: In the first patient, beard growth was significant by 3 months of treatment. By 6 months of treatment, hair growth was apparent throughout the entire body. By 8 months of treatment, scalp hair continued to grow longer and thicker. In addition, eyelashes and eyebrows were established. In the second patient, a noticeable increase in scalp hair was present just 1 month into treatment. By 4 months into treatment, significant scalp regrowth was observed as well as eyelash, eyebrow and beard regrowth. Axillary hair regrowth and isolated leg hair was noted by 8 months. CONCLUSION: In our patients, tofacitinib successfully alleviated AU in the absence of significant adverse side-effects. We recommend that further study be required to establish safety and confirm efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".