Use of Hydroxychloroquine and Risk of Heart Failure in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between the use of hydroxychloroquine (HCQ) and risk of developing heart failure (HF) in rheumatoid arthritis (RA). METHODS: In this nested case-control study, cases were Olmsted County, Minnesota residents with incident RA (based on 1987 American College of Rheumatology criteria) from 1980 to 2013 who developed HF after RA incidence. Each case was matched on year of birth, sex, and year of RA incidence with an RA control who did not develop HF. Data on HCQ use including start and stop dates, as well as dose changes, were reviewed and used to calculate HCQ duration and cumulative dose. Age-adjusted logistic regression models were used to examine the association between HCQ and HF. RESULTS: The study identified 143 RA cases diagnosed with HF (mean age 65.8 yrs, 62% females) and 143 non-HF RA controls (mean age 64.5, 62% female). HCQ cumulative dose was not associated with HF (OR 0.96 per 100-g increase in cumulative dose, 95% CI 0.90-1.03). Likewise, no association was found for patients with a cumulative dose ≥ 300 g (OR 0.92, 95% CI 0.41-2.08). The HCQ duration of intake in years prior to index was not associated with HF (OR 0.98, 95% CI 0.91-1.05). CONCLUSION: Use of HCQ was not associated with development of HF in patients with RA in this study. Further studies are needed to understand the effect of higher doses of HCQ on the development of HF in RA.
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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) | 0.000 | 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 | 0.000 | 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".