Opioid Use among Patients with Early Inflammatory Arthritides Compared to the General Population
Bibliographic record
Abstract
OBJECTIVE: To assess to what extent the worldwide opioid epidemic affects Finnish patients with early inflammatory arthritis (IA). METHODS: From the nationwide register maintained by the Social Insurance Institution of Finland, we collected all incident adult patients with newly onset seropositive and seronegative rheumatoid arthritis (RA+ and RA-) and undifferentiated arthritis (UA) between 2010 and 2014. For each case, 3 general population (GP) controls were matched according to age, sex, and place of residence. Drug purchases between 2009 and 2015 were evaluated 1 year before and after the index date (date of IA diagnosis), further dividing this time into 3-month periods. RESULTS: A total of 12,115 patients (66% women) were identified. At least 1 opioid purchase was done by 23-27% of the patients 1 year before and 15-20% one year after the index date. Relative risk (RR) of opioid purchases compared to GP was highest during the last 3-month time period before the index date [RR 2.81 (95% CI 2.55-3.09), 3.06 (2.68-3.49), and 4.04 (3.51-4.65) for RA+, RA-, and UA, respectively] but decreased after the index date [RR 1.38 (1.23-1.58), 1.91 (1.63-2.24), and 2.51 (2.15-2.93)]. Up to 4% of the patients were longterm users both before and after the diagnosis. CONCLUSION: During 2009-15 in Finland, opioid use peaked just before the diagnosis of IA but decreased rapidly after that, suggesting effective disease control, especially in seropositive RA. Further, opioids were used to treat arthritis pain of patients with incident RA and UA less often than previously reported from other countries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".