Retinal toxicity in a multinational inception cohort of patients with systemic lupus on hydroxychloroquine
Bibliographic record
Abstract
OBJECTIVE: To evaluate hydroxychloroquine (HCQ)-related retinal toxicity in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort. METHODS: Data were collected at annual study visits between 1999 and 2019. We followed patients with incident SLE from first visit on HCQ (time zero) up to time of retinal toxicity (outcome), death, loss-to-follow-up or end of study. Potential retinal toxicity was identified from SLICC Damage Index scores; cases were confirmed with chart review. Using cumulative HCQ duration as the time axis, we constructed univariate Cox regression models to assess if covariates (ie, HCQ daily dose/kg, sex, race/ethnicity, age at SLE onset, education, body mass index, renal damage, chloroquine use) were associated with HCQ-related retinal toxicity. RESULTS: We studied 1460 patients (89% female, 52% white). Retinal toxicity was confirmed in 11 patients (incidence 1.0 per 1000 person-years, 0.8% overall). Average cumulative time on HCQ in those with retinal toxicity was 7.4 (SD 3.2) years; the first case was detected 4 years after HCQ initiation. Risk of retinal toxicity was numerically higher in older patients at SLE diagnosis (univariate HR 1.05, 95% CI 1.01 to 1.09). CONCLUSIONS: This is the first assessment of HCQ and retinal disease in incident SLE. We did not see any cases of retinopathy within the first 4 years of HCQ. Cumulative HCQ may be associated with increased risk. Ophthalmology monitoring (and formal assessment of cases of potential toxicity, by a retinal specialist) remains important, especially in patients on HCQ for 10+ years, those needing higher doses and those of older age at SLE diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".