Disease Activity, Smoking, and Reproductive-related Predictors of Poor Prognosis in Patients with Very Early Inflammatory Polyarthritis
Bibliographic record
Abstract
OBJECTIVE: To identify disease activity, smoking, and reproductive-related predictors of a poor prognosis in patients with very early inflammatory polyarthritis (IP). METHODS: Patients with very early IP (symptom duration 4-11 weeks) included in our study were participants in the STIVEA (Steroids In Very Early Arthritis) randomized placebo-controlled trial. At baseline, disease-related variables were measured and patients were asked to complete a questionnaire covering smoking status and reproductive questions. Baseline predictors of poor prognosis [i.e., the need to start disease-modifying antirheumatic drug (DMARD) therapy by 6 months or the clinical diagnosis of rheumatoid arthritis (RA) at 12 months] were identified, applying logistic regression analyses adjusted for treatment group. RESULTS: Rheumatoid factor (RF) positivity was one of the strongest clinical predictors of a poor prognosis: OR for DMARD therapy at 6 months, 4.00 (95% CI 2.00-8.00) and OR for a diagnosis of RA at 12 months, 9.48 (95% CI 4.48-20.07). There was a significant association between current smoking at baseline compared to never smoking and a diagnosis of RA at 12 months (OR 3.15, 95% CI 1.16-8.56). CONCLUSION: About 6 in 7 patients with very early RF-positive IP were diagnosed with RA 1 year later. In addition, 1 in 4 IP patients who smoke will develop RA later. It is recommended to treat RF-positive patients who have IP with DMARD at presentation and to advise patients to stop smoking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".