Risks of ophthalmic disorders in patients with systemic lupus erythematosus – a secondary cohort analysis of population-based claims data
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus (SLE) can directly affect various part of the ocular system, but there was no comprehensive analysis of ophthalmic disorders of patients with SLE using population-based data. The aim of this study was to investigate the frequency and prevalence of ophthalmic disorders for ophthalmologist visits in adult patients with SLE and to evaluate the risk of dry eye syndrome, cataracts, glaucoma, episcleritis and scleritis, and retinal vascular occlusion in these patients. METHODS: The Taiwan's National Health Insurance Research Database was used to assemble a SLE cohort consisting of newly diagnosed SLE between 2000 and 2012. A comparison cohort was also sampled from the same database and it consisted of 10 patients without SLE for each patient with SLE, based on frequency matching for sex, five-year age interval, and index year. Both cohorts were followed until either the study outcomes have occurred or the end of the follow-up period. RESULTS: Patients with SLE (n = 521) exhibited a significantly higher prevalence (68.1% vs. 60.5%, P = 0.001) and frequency (median 5.51 vs. 1.71 per 10 years, P < 0.001) for outpatient ophthalmologist visits compared with patients without SLE. The risk of dry eye syndrome (adjusted incidence rate ratio [IRR] 4.45, P < 0.001), cataracts (adjusted IRR 3.18, P < 0.001), and glaucoma (adjusted IRR 2.23, P = 0.002) were significantly higher in patients with SLE. In addition, the risk of several SLE related ophthalmic disorders, including episcleritis and scleritis (adjusted IRR 6.11, P < 0.001) and retinal vascular occlusion (adjusted IRR 3.81, P = 0.023) were significantly higher in patients with SLE. CONCLUSIONS: The increased risk of dry eye syndrome, cataracts, glaucoma, episcleritis and scleritis, and retinal vascular occlusion in patients with SLE deserves vigilance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".