Adherence to Antimalarial Therapy and Risk of Type 2 Diabetes Mellitus Among Patients With Systemic Lupus Erythematosus: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between adherence to antimalarials and type 2 diabetes mellitus (DM) in patients with systemic lupus erythematosus (SLE). METHODS: Using administrative health databases in British Columbia, Canada, we conducted a retrospective, longitudinal cohort study of patients with incident SLE and incident antimalarial use. We established antimalarial drug courses by defining a new course when a 90-day gap is exceeded between refills and we calculated proportion of days covered (PDC) for each course. We categorized medication taking as: 1) adherent (PDC ≥0.90), 2) nonadherent (0 < PDC < 0.90), and 3) discontinuer (no drug). Type 2 DM outcomes were based on outpatient or inpatient visits, or antidiabetic medication use. We used multivariable Cox proportional hazards models with time-dependent variables. RESULTS: Over a median of 4.62 years of follow-up in our incident cohort of 1,498 patients with SLE (90.8% women), we recorded 140 incident cases of type 2 DM. Multivariable hazard ratios were 0.61 (95% confidence interval [95% CI] 0.40-0.93) for adherent and 0.78 (95% CI 0.50-1.22) for nonadherent, respectively, as compared to discontinuers. CONCLUSION: Our findings of a protective effect of adherence to antimalarials in preventing type 2 DM provides further support for the importance of adherence to antimalarials to obtain the benefits of therapy.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".