Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it