What causes alopecia areata?
Bibliographic record
Abstract
The pathobiology of alopecia areata (AA), one of the most frequent autoimmune diseases and a major unsolved clinical problem, has intrigued dermatologists, hair biologists and immunologists for decades. Simultaneously, both affected patients and the physicians who take care of them are increasingly frustrated that there is still no fully satisfactory treatment. Much of this frustration results from the fact that the pathobiology of AA remains unclear, and no single AA pathogenesis concept can claim to be universally accepted. In fact, some investigators still harbour doubts whether this even is an autoimmune disease, and the relative importance of CD8(+) T cells, CD4(+) T cells and NKGD2(+) NK or NKT cells and the exact role of genetic factors in AA pathogenesis remain bones of contention. Also, is AA one disease, a spectrum of distinct disease entities or only a response pattern of normal hair follicles to immunologically mediated damage? During the past decade, substantial progress has been made in basic AA-related research, in the development of new models for translationally relevant AA research and in the identification of new therapeutic agents and targets for future AA management. This calls for a re-evaluation and public debate of currently prevalent AA pathobiology concepts. The present Controversies feature takes on this challenge, hoping to attract more skin biologists, immunologists and professional autoimmunity experts to this biologically fascinating and clinically important model 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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".