Allergy promotes alopecia areata in a subset of patients
Bibliographic record
Abstract
In this commentary, we focus on allergy as a facilitating factor in the pathogenesis of alopecia areata (AA). From previous studies on AA, it is well known that subsets of patients can have one or more of; seasonal relapse, comorbid atopic rhinitis, asthma and dermatitis, lesion infiltrating eosinophils and plasma cells, high levels of total IgE, specific IgE for house dust mites (HDMs), and/or disrupted skin barrier function by the evaluation of filaggrin. Allergy and AA share a similar genetic background; both contributing to an immune reaction imbalance. Furthermore, adjunctive treatment with antihistamines, or desensitization for HDM, can reduce the severity of alopecia in atopic AA patients. Therefore, allergies may contribute to the onset and relapse of AA. Identification of an allergic or atopic immune component in AA patient subsets may indicate adjunctive treatment intervention measures against allergies should be taken which may improve the success of conventional AA treatment.
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) | 0.000 | 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) | 0.003 | 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 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".