The Early Protective Effect of Hydroxychloroquine on the Risk of Cumulative Damage in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To assess whether hydroxychloroquine (HCQ) prevents early damage in patients with systemic lupus erythematosus (SLE). METHODS: We updated an existing systematic review of literature on clinical effects of HCQ in patients with SLE. We conducted a nested case-control study embedded in an inception cohort of patients with SLE. Systemic Lupus International Collaborating Clinics Damage Index (SDI) at 3 years was considered as our primary outcome. Patients with SDI > 0 at 3 years were considered cases and patients with SDI = 0 were controls. Cases and controls were first compared by univariate analysis. Then conditional logistic regression models adjusting for potential confounders were done to study the effect of HCQ on damage accrual. RESULTS: Included in the analysis were 481 patients who had 3 or more years of followup. Out of this cohort, we could match 151 cases with 151 controls. Univariate analysis identified age, the use of any immunosuppressive drugs, HCQ, and cumulative dose of steroids as significant covariates associated with damage accrual. In multivariate analysis, the use of HCQ remained significantly associated with less damage (OR 0.34, 95% CI 0.132-0.867), while age (OR 1.05, 95% CI 1.027-1.078) and a variable combining SLE activity and steroid dose (OR 1.73, 95% CI 1.306-2.295) were associated with damage at 3 years. CONCLUSION: We demonstrated that HCQ use was associated with less damage at 3 years after diagnosis of SLE when attention was given and adjustment done for disease activity and steroid dose, duration of disease, and calendar year of 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".