Risk Factors for Diagnosis of Psoriatic Arthritis, Psoriasis, Rheumatoid Arthritis, and Ankylosing Spondylitis: A Set of Parallel Case-control Studies
Bibliographic record
Abstract
OBJECTIVE: To compare potential risk factors for the diagnosis of psoriatic arthritis (PsA), psoriasis (PsO), rheumatoid arthritis (RA), and ankylosing spondylitis (AS). METHODS: Four parallel case-control studies were conducted within The Health Improvement Network using data between 1994 and 2015. Patients with PsA, PsO, RA, or AS were identified using validated code lists and matched to controls on age, sex, practice, and year. Risk factors were selected in the time prior to diagnosis. Multivariable logistic regression models were constructed for each disease using automated stepwise regression to test potential risk factors. RESULTS: Patients with incident PsA (n = 7594), PsO (n = 111,375), RA (n = 28,341), and AS (n = 3253) were identified and matched to 75,930, 1,113,345, 283,226, and 32,530 controls, respectively. Median diagnosis age was 48 (IQR 38-59), 43 (IQR 28-60), 60 (IQR 48-71), and 41 (IQR 32-54) years, respectively. In multivariable models, there were some shared and some differing risk factors across all 4 diseases: PsA was associated with obesity, pharyngitis, and skin infections; PsA and PsO were associated with obesity and moderate alcohol intake; PsA and AS were associated with uveitis; and PsA and RA were associated with preceding gout. Both RA and AS were associated with current smoking, former moderate drinking, anemia, osteoporosis, and inflammatory bowel disease. All shared former or current smoking as a risk factor; statin use was inversely associated with all 4 diseases. CONCLUSION: Shared and different risk factors for PsA, PsO, RA, and AS were identified. Statin use was inversely associated with all 4 conditions.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".