Sodium glucose co‐transporter 2 inhibitors versus dipeptidyl peptidase‐4 inhibitors and the risk of ventricular arrhythmia among patients with type 2 diabetes: A population‐based cohort study
Bibliographic record
Abstract
AIMS: To determine whether sodium glucose co-transporter 2 inhibitors (SGLT2i) use, compared with dipeptidyl peptidase-4 inhibitors (DPP4i) use, is associated with the risk of ventricular arrhythmias (VA) among patients with type 2 diabetes. MATERIALS AND METHODS: We conducted a population-based cohort study using a prevalent new-user design and data from the United Kingdom Clinical Practice Research Datalink Aurum, linked to hospitalisation and vital statistics data. SGLT2i users were matched to DPP4i users on diabetes treatment intensity, duration of prior DPP4i use, calendar time, sex, age, and time-conditional propensity score. Cox models estimated the hazard ratio (HR) and corresponding 95% confidence intervals (CI) for VA with SGLT2i vs. DPP4i use. Secondary analyses stratified by SGLT2i user type. Secondary outcomes included fatal VA and cardiac arrest. RESULTS: Among 88 516 matched patients, 385 VA events occurred over a mean follow-up of 0.86 years (25.3 per 10 000 person-years). Overall, SGLT2i use was not associated with the risk of VA (HR: 0.88, 95% CI: 0.71-1.07). There was no association among incident new users (HR: 0.98, 95% CI: 0.76-1.27) but SGLT2i use was associated with a lower risk among prevalent new users (HR: 0.65, 95% CI: 0.46-0.92). SGLT2i use was associated with a lower risk of cardiac arrest (HR: 0.64, 95% CI: 0.49-0.83); our analysis of fatal VA was inconclusive due to sparse data (HR: 1.76, 95% CI: 0.47-6.64). CONCLUSIONS: SGLT2i use was not associated with the risk of VA among patients with type 2 diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 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".