Opioid Use and Risk of Nonvertebral Fractures in Adults With Rheumatoid Arthritis: A Nested Case–Control Study Using Administrative Databases
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of nonvertebral fractures in patients with rheumatoid arthritis (RA) who were exposed to opioids. METHODS: A population-based, nested case-control study was conducted using health services administrative databases (Quebec, Canada) from 1997 to 2012. Among RA patients, cases of nonvertebral fractures from 2007 to 2012 were identified using a validated algorithm. The date of the first fracture was the index date for the case and his/her matched control. Controls were selected using incidence density sampling and were matched 5:1 to cases for age, sex, and date of RA diagnosis. Opioid exposure was classified as current use, recent past use, remote past use, and nonuse. Conditional logistic regression was used to assess the association of nonvertebral fractures with opioid exposure, adjusting for comorbidity, indicators of RA severity, drugs influencing fracture risk, and health care utilization. RESULTS: In total, 1,723 cases and 8,046 controls were identified. Among these patients, 2,595 (722 cases and 1,873 controls) had been exposed to opioids. Current use (versus nonuse) increased the risk of nonvertebral fracture. Cumulative current use of opioids according to the quartile distribution was also associated with the risk of nonvertebral fracture: for continuous use for 1-20 days before the index date, odds ratio (OR) 11.49 (95% confidence interval [95% CI] 8.81-14.99); for 21-155 days, OR 1.75 (95% CI 1.31-2.33); for 156-355 days, OR 1.54 (95% CI 1.17-2.04); and for ≥356 days, OR 1.73 (95% CI 1.31-2.30). No association between the risk of nonvertebral fractures and recent past use or remote past use of opioids was observed. CONCLUSION: Among RA patients, the risk of nonvertebral fracture is increased in those treated with opioids.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".