Effects of disease-modifying antirheumatic drugs on nonvertebral fracture risk in rheumatoid arthritis: A population-based cohort study
Bibliographic record
Abstract
Several prior investigations demonstrate an improvement in bone mineral density associated with use of tumor necrosis factor inhibitors (TNFi). We compared the risk of osteoporotic fractures among patients with rheumatoid arthritis (RA) initiating a disease-modifying antirheumatic drug (DMARD). A population-based cohort study was conducted using health care utilization data (1996-2008) from a Canadian province and a U.S. commercial insurance plan. Patients with at least two RA diagnoses were identified, and follow-up began with the first prescription for a DMARD. Drug regimens were categorized into three mutually exclusive hierarchical groups: (1) TNFi with or without nonbiologic DMARDs (nbDMARD), (2) methotrexate (MTX) without a TNFi, or (3) other nbDMARD without a TNFi or MTX. Main outcomes were hospitalizations for fractures of the hip, wrist, humerus, or pelvis based on diagnoses and procedure codes. The study cohort consisted of 16,412 RA patients with 25,988 new treatment episodes: 5856 TNFi, 12,554 MTX, and 7578 other nbDMARD. The incidence rate per 1000 person-years for osteoporotic fracture were 5.11 [95% confidence interval (CI) 3.50-7.45] for TNFi, 5.35 (95% CI 4.08-7.02) for MTX, and 6.38 (95% CI 3.78-10.77) for other nbDMARD. After multivariable adjustment for osteoporosis and fracture-related risk factors, the risk of nonvertebral osteoporotic fracture was not different in either TNFi [hazard ratio (HR) 1.07, 95% CI 0.57-1.98] or MTX (HR 1.18, 95% CI 0.60-2.34) compared with nbDMARD. Among subjects diagnosed with RA, the adjusted risk of nonvertebral fracture was similar across persons starting a TNFi, MTX, or other nbDMARD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".