Bibliographic record
Abstract
A 3500-year-old papyrus from ancient Egypt provides a list of treatments for many diseases including "bite hair loss," most likely alopecia areata (AA). The treatment of AA remained largely unchanged for over 1500 years. In 30 CE, Celsus described AA presenting as scalp alopecia in spots or the "windings of a snake" and suggested treatment with caustic compounds and scarification. The first "modern" description of AA came in 1813, though treatment still largely employed caustic agents. From the mid-19th century onwards, various hypotheses of AA development were put forward including infectious microbes (1843), nerve defects (1858), physical trauma and psychological stress (1881), focal inflammation (1891), diseased teeth (1902), toxins (1912) and endocrine disorders (1913). The 1950s brought new treatment developments with the first use of corticosteroid compounds (1952), and the first suggestion that AA was an autoimmune disease (1958). Research progressively shifted towards identifying hair follicle-specific autoantibodies (1995). The potential role of lymphocytes in AA was made implicit with immunohistological studies (1980s). However, studies confirming their functional role were not published until the development of rodent models (1990s). Genetic studies, particularly genome-wide association studies, have now come to the forefront and open up a new era of AA investigation (2000s). Today, AA research is actively focused on genetics, the microbiome, dietary modulators, the role of atopy, immune cell types in AA pathogenesis, primary antigenic targets, mechanisms by which immune cells influence hair growth, and of course the development of new treatments based on these discoveries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".