Finasteride-mediated hair regrowth and reversal of atrophy in a patient with frontal fibrosing alopecia
Bibliographic record
Abstract
Frontal fibrosing alopecia (FFA) is a form of cicatricial alopecia that predominantly affects perimenopuasal and postmenopausal women.1, 2, 3, 4 Although the precise cause is unknown, it is currently classified as a primary lymphocytic cicatricial alopecia that is closely related to lichen planopilaris (LPP). FFA not only causes scarring hair loss but also frequently causes skin atrophy within the frontal hairline.5, 6 Until recently, the treatment for FFA has mirrored the treatment algorithms used for other primary lymphocytic scarring alopecias. Topical steroids, steroid injections, hydroxychloroquine, doxycycline, tetracycline and mycophenolate mofetil have been the main treatments. However, in the last few years, an increasing number of reports have suggested a beneficial role for the 5 alpha reductase inhibitory medications, finasteride and dutasteride.4, 6, 7, 8 To date, the published studies of FFA treatment outcomes have focused on hair follicles—whether they are lost, stabilized, or promoted to regrow. The other important feature of the condition—cutaneous atrophy—has not received much attention. Here, I report a patient with FFA who experienced not only marked frontal hair regrowth with the 5α-reductase inhibitor, finasteride, but also a marked reversal of cutaneous atrophy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".