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
BACKGROUND: Alopecia areata (AA) is a non-scarring hair loss disorder of autoimmune etiology. OBJECTIVE: To familiarize physicians with the clinical presentation, diagnosis, evaluation, and management of pediatric alopecia areata. METHODS: The search term "Alopecia areata" was entered into a Pubmed search. A narrow scope was applied to the categories of "epidemiology", "clinical diagnosis", "investigations", "comorbidities", and "treatment". Meta-analyses, randomized controlled trials, clinical trials, observational studies, and reviews were included. Only papers published in the English language were included. A descriptive, narrative synthesis was provided of the retrieved articles. RESULTS: AA is an autoimmune disease of unknown etiology. It is the third most common dermatologic presentation in children with a lifetime risk of 1-2%. Diagnosing AA can be made on the basis of the history and clinical findings. Patients will often present with patchy, non-scarring hair loss, generally affecting the scalp. History may reveal a personal or family medical history of autoimmune or atopic disease or a recent stressful event. Tricoscopic examination will classically show "exclamation point hairs" and "yellow dots". Nonspecific nail changes may be present. Other clinical variants include alopecia totalis, alopecia universalis, ophiasis, sisaipho, and Canitis subita. There are multiple treatment options for AA, including conservative treatment, and topical, oral, and injectable medications. CONCLUSION: AA is an autoimmune disease with a heterogeneous presentation and unpredictable clinical course. Although there is no cure for AA, there are many current treatment options available to help manage this disfiguring disease.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
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