Case of alemtuzumab-related alopecia areata management in MS
Bibliographic record
Abstract
We report a case of alopecia associated with alemtuzumab treatment for MS, the third case described in the literature, to our knowledge.1,2 A 31-year-old woman diagnosed with relapsing-remitting MS in February 2015 presented with demyelinating lesions in the medulla, cerebellar peduncle, cerebellar hemisphere in addition to >9 T2 supratentorial lesions on brain MRI. She was treated with an MS disease-modifying therapy (dimethyl fumarate 240 mg twice a day) for 8 months and experienced 2 clinical relapses during this time, prompting escalation to treatment with alemtuzumab (60 mg) in June 2016. Her second course of treatment with alemtuzumab (36 mg) occurred in June 2017, with lymphocyte count reaching 0.5 × 109/L 30 days posttreatment. Following alemtuzumab treatment, the patient was healthy without clinical or radiologic MS disease activity and had no other medical concerns. Two months after second course of treatment with alemtuzumab, the patient had a lymphocyte count of 0.7 × 109/L and experienced significant patchy hair loss, and a dermatologist confirmed the diagnosis of alopecia areata (AA). Hair loss continued over a period of 5 months, during which her lymphocyte count ranged from 0.7 to 0.8 × 109/L. She had a positive hair pull test, indicating further hair loss, despite treatment with intralesional scalp injections of triamcinolone acetonide (5 mg/cc, 3 cc total) in most of the affected areas both on AA diagnosis and 1 month later. Six months after the second course of alemtuzumab, the patient experienced a sensory partial transverse myelitis relapse, which was acutely managed with intravenous methylprednisolone 1,000 mg for 5 days. Within 4 weeks after her systemic steroid treatment, hair regrowth including regions of depigmentation was noted. The patient's lymphocyte count during this time was 0.9 × 109/L. Within 4 months of steroid treatment, hair regrowth was present over the entire scalp (figure). The authors thank Krista Barclay for her help in managing the patient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".