Systemic Lupus Erythematosus May Be a Risk Factor for <scp>Antimalarial‐Induced</scp> Retinopathy Compared With Other Rheumatologic Diseases
Bibliographic record
Abstract
OBJECTIVE: To describe the pattern and risk factors for antimalarial (AM)-induced retinopathy in patients with rheumatic diseases. METHODS: A retrospective chart review was conducted at an urban Canadian center for patients with AM use of more than 3 months and documented retinopathy screening. Univariate and multivariate regression analyses were performed to determine risk factors for retinopathy. Sensitivity analyses included stratification of analysis by method of screening and by hydroxychloroquine (HCQ) versus chloroquine (CQ). RESULTS: A total of 613 patients were included in the final analysis, with systemic lupus erythematosus (SLE) (n = 259) as the most common diagnosis. Definite AM-induced retinal toxicity was observed in 12 patients, 11 of whom had SLE. The earliest diagnosis of toxicity occurred after 5.4 years of AM therapy, and the prevalence beyond 5 years was 3.1%. In univariate analysis, a diagnosis of SLE (P = 0.009; odds ratio [OR]: 15.66; 95% confidence interval [CI]: 2.01-122.05), the daily weight-based dose of HCQ (P = 0.044; OR: 1.49; 95% CI: 1.01-2.20), cumulative CQ dose (P = 0.014; OR: 4.80; CI: 1.37-16.84), and daily CQ weight-based dose (P = 0.0001; OR: 5.70; 95% CI: 2.41-13.49) were significantly associated with toxicity. In multivariate analysis, diagnosis of SLE (P = 0.022; OR: 12.14; 95% CI: 1.44-102.44) and daily CQ weight-based dose (P = 0.005; OR: 1.83; 95% CI: 1.83-26.75) were significant after adjusting for standard covariates. CONCLUSION: The risk of AM-induced retinopathy increases after 5 years of therapy. There may be higher rates of toxicity in patients with SLE because of longer duration of treatment, higher weight-based dosages, and more CQ use in this population, and SLE may be an independent risk factor.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".