Asthma and multiple sclerosis: an inverse association in a case-control general practice population
Bibliographic record
Abstract
BACKGROUND: Th1/Th2 imbalance is hypothesized to up-regulate some diseases and down-regulate others. Compared to controls, multiple sclerosis (MS) (Th1-mediated) has been linked to a reduced risk of allergy and asthma (Th2-mediated), based on patient questionnaire studies and a review of asthma medication. AIM: To investigate whether MS is associated with a reduced risk of Th2-associated diseases and an increased risk of Th1-associated diseases. DESIGN: Retrospective matched case-control study. METHODS: Three hundred and twenty MS patients and controls matched for age, gender, location and smoking were selected from the Welsh General Practice Morbidity Database from 1995-99. Case and control records were assessed for Th1-mediated and Th2-mediated diseases. RESULTS: Overall, 346 MS patients were identified, giving a prevalence of 127 per 100 000. There was an inverse relationship between multiple sclerosis (MS) and asthma (OR 0.33; 95%CI 0.15-0.77). No statistically significant relationships emerged between other Th2-associated (eczema, dermatitis) or any Th1-associated (rheumatoid arthritis, thyroid disorders, inflammatory bowel disease [IBD], type 1 diabetes) diseases and MS, although no patient in either group had treated type 1 diabetes. A trend existed for IBD, with 5/320 of cases affected and no controls; OR infinity; 95%CI 1.30-infinity; p=0.063. DISCUSSION: This inverse association between MS and asthma is compatible with a Th1/Th2 imbalance. Although the Th1/Th2 theory is probably an over-simplification in MS, a shift from Th1 cytokine dominance towards Th2 may provide drug-targeting routes for MS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".