Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with a background understanding of the therapeutic regimens and treatment outcomes for alopecia areata (AA), as well as to help identify those patients for whom dermatologist referral might be required. SOURCES OF INFORMATION: PubMed was searched for relevant articles regarding the treatment of AA. MAIN MESSAGE: Alopecia areata is a form of autoimmune hair loss affecting both children and adults. While there is no associated mortality with the disease, morbidity from the psychological effects of hair loss can be devastating. Upon identification of AA and the disease subtype, an appropriate therapeutic regimen can be instituted to help halt hair loss or possibly initiate hair regrowth. First-line treatment involves intralesional triamcinolone with topical steroids or minoxidil or both. Primary care physicians can safely prescribe and institute these treatments. More advanced or refractory cases might require oral immunosuppressants, topical diphenylcyclopropenone, or topical anthralin. Eyelash loss can be treated with prostaglandin analogues. Those with extensive loss might choose camouflaging options or a hair prosthesis. It is important to monitor for psychiatric disorders owing to the profound psychological effects of hair loss. CONCLUSION: Family physicians will encounter many patients experiencing hair loss. Recognition of AA and an understanding of the underlying disease process will allow an appropriate therapeutic regimen to be instituted. More advanced or refractory cases need to be identified, allowing for an appropriate dermatologist referral when necessary.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".