Alopecia areata: Part 1: pathogenesis, diagnosis, and prognosis.
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 provide family physicians with a background understanding of the epidemiology, pathogenesis, histology, and clinical approach to the diagnosis of alopecia areata (AA). SOURCES OF INFORMATION: PubMed was searched for relevant articles regarding the pathogenesis, diagnosis, and prognosis of AA. MAIN MESSAGE: Alopecia areata is a form of autoimmune hair loss with a lifetime prevalence of approximately 2%. A personal or family history of concomitant autoimmune disorders, such as vitiligo or thyroid disease, might be noted in a small subset of patients. Diagnosis can often be made clinically, based on the characteristic nonscarring, circular areas of hair loss, with small "exclamation mark" hairs at the periphery in those with early stages of the condition. The diagnosis of more complex cases or unusual presentations can be facilitated by biopsy and histologic examination. The prognosis varies widely, and poor outcomes are associated with an early age of onset, extensive loss, the ophiasis variant, nail changes, a family history, or comorbid autoimmune disorders. CONCLUSION: Alopecia areata is an autoimmune form of hair loss seen regularly in primary care. Family physicians are well placed to identify AA, characterize the severity of disease, and form an appropriate differential diagnosis. Further, they are able educate their patients about the clinical course of AA, as well as the overall prognosis, depending on the patient subtype.
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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) | 0.000 | 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