What Can We Learn from Animal Models of Alopecia areata?
Bibliographic record
Abstract
Alopecia areata (AA) is a hair loss disease marked by a focal inflammatory infiltrate of dystrophic anagen stage hair follicles by CD4+ and CD8+ lymphocytes. Although AA is thought to be an autoimmune disorder, definitive proof is lacking. Moreover, characterization of the primary pathogenic mechanisms by which hair loss is induced in AA is limited. In this context, animal models may provide a vital contribution to understanding AA. Recent research using animal models of AA has focused on providing evidence in support of a lymphocyte-mediated pathogenic mechanism consistent with AA as an autoimmune disease. In the future, research with both humans and animal models shall likely concentrate on identifying the primary antigenic epitopes involved in AA and the genetics of AA susceptibility. With a comprehensive understanding of the key elements in AA pathogenesis, new avenues for therapeutic research and intervention will be defined.
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How this classification was reachedexpand
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) | 0.003 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".