Impact of Antimalarial Adherence on Mortality Among Patients With Newly Diagnosed Systemic Lupus Erythematosus: A <scp>Population‐Based</scp> Cohort Study
Bibliographic record
Abstract
Objective To assess the association of antimalarial (AM) adherence with premature mortality among incident systemic lupus erythematosus (SLE) patients. Methods All patients with incident SLE and incident AM use in British Columbia, Canada, between January 1997 and March 2015 were identified using the provincial administrative databases. Follow‐up started on the first day of having both SLE and AM. The outcome was all‐cause mortality. An adherence measure, proportion of days covered (PDC), was calculated and categorized as adherent (PDC ≥ 0.90), nonadherent (0 < PDC < 0.90), and discontinuer (PDC = 0) during 30‐day windows. We first used Cox models for time‐to‐death, adjusting for baseline and time‐varying confounders on medication usages, health care utilization, and comorbidities. We then used marginal structural Cox models via inverse probability weighting designed for causal inference with time‐varying confounders to assess the effect of AM adherence on premature mortality. Results We identified 3,062 individuals with incident SLE and incident AM use (mean age 46.9 years). Over the mean follow‐up period of 6.4 years, 242 (7.9%) of those patients died. Adjusted hazard ratios (HRadj) from the Cox model for AM adherent and nonadherent SLE patients were 0.20 (95% confidence interval [95% CI] 0.13–0.29) and 0.62 (95% CI 0.42–0.91), respectively, compared to discontinuers. The corresponding HRadj from the marginal structural Cox model were 0.17 (95% CI 0.12–0.25) and 0.58 (95% CI 0.40–0.85), respectively. A significant trend in the HRadj of mortality risk over the adherence levels was found (P < 0.001). Conclusion Patients with SLE adhering to AM therapy had a 71% and 83% lower risk of death than patients who do not adhere or who discontinued AMs, respectively.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".