Bibliographic record
Abstract
OBJECTIVE: To describe an organized diagnostic approach for both nonscarring and scarring alopecias to help family physicians establish an accurate in-office diagnosis. To explain when ancillary laboratory workup is necessary to confirm the diagnosis. QUALITY OF EVIDENCE: Current diagnostic and therapeutic interventions for hair loss are based on randomized controlled studies, uncontrolled studies, and case series. MEDLINE was searched from January 1966 to December 1998 with the MeSH words alopecia, hair, and alopecia areata. Articles were selected on the basis of experimental design, with priority given to the most current large multicentre controlled studies. Overall global evidence for therapeutic intervention for hair loss is quite strong. MAIN MESSAGE: The most common forms of nonscarring alopecias are androgenic alopecia, telogen effluvium, and alopecia areata. Other disorders include trichotillomania, traction alopecia, tinea capitis, and hair shaft abnormalities. Scarring alopecia is caused by trauma, infections, discoid lupus erythematosus, or lichen planus. Key to establishing an accurate diagnosis is a detailed history, including medication use, systemic illnesses, endocrine dysfunction, hair-care practices, and family history. All hair-bearing sites should be examined. A 4-mm punch biopsy of the scalp is useful, particularly to diagnose scarring alopecias. Once a diagnosis has been established, specific therapy can be initiated. CONCLUSIONS: Diagnosis and management of hair loss is an interesting challenge for family physicians. An organized approach to recognizing characteristic differential features of hair loss disorders is key to diagnosis and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".