Atorvastatin is unlikely to prevent rheumatoid arthritis in high risk individuals: results from the prematurely stopped STAtins to Prevent Rheumatoid Arthritis (STAPRA) trial
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: Persons at high risk of rheumatoid arthritis (RA) might benefit from a low-risk pharmacological intervention aimed at primary prevention. Previous studies demonstrated disease-modifying effects of statins in patients with RA as well as an association between statin use and a decreased risk of RA development. A randomised, double-blind, placebo-controlled trial investigated whether atorvastatin could prevent arthritis development in high-risk individuals. METHODS: Arthralgia patients with anticitrullinated protein antibody (ACPA) >3 xULN or ACPA and rheumatoid factor, without (a history of) arthritis, were randomised to receive atorvastatin 40 mg daily or placebo for 3 years. The calculated sample size was 220 participants. The primary endpoint was clinical arthritis. Cox regression analysis was used to determine the effect of atorvastatin on arthritis development. RESULTS: Due to a low inclusion rate, mainly because of an unwillingness to participate, the trial was prematurely stopped. Data of the 62 randomised individuals were analysed. Median follow-up was 14 (inner quartiles 6-35) months. Fifteen individuals (24%) developed arthritis: 9/31 (29%) in the atorvastatin group; 6/31 (19%) in the placebo group: HR 1.40, 95% CI 0.50 to 3.95. CONCLUSIONS: In this small set of randomised high-risk individuals, we did not demonstrate a protective effect of atorvastatin on arthritis development. The main reason for the low inclusion was unwillingness to participate; this may also impede other RA prevention trials. Further research to investigate and solve barriers for prevention trial participation is needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it