Insulin use in type 2 diabetes and the risk of dementia: A comparative population-based cohort study.
Bibliographic record
Abstract
Objective: Evidence of an increased dementia risk with insulin use in type 2 diabetes (T2DM) is weakened by confounding by indication and disease severity. Herein we reassess this association, while accounting for confounding through design and analysis. Research Design and Methods: Using administrative healthcare data from British Columbia, Canada we identified patients diagnosed with T2DM in 1998-2016. To adjust for confounding by diabetes severity through design, we compared new users of insulin to new users of a non-insulin class, both from a restricted cohort of those who previously received two non-insulin antihyperglycemic classes. We further adjusted for confounding using: (1) conventional multivariable adjustment and (2) inverse probability of treatment weighting (IPTW) based on the high-dimensional propensity score algorithm. The hazard ratio [HR] (95% confidence interval [CI]) of dementia was estimated using cause-specific hazards models with death as a competing risk. Results: The analytical comparative cohort included 7,863 insulin vs 25,230 non-insulin users. At baseline, insulin users were more likely to have worse health indicators. A total of 78 dementia events occurred over a median (IQR) follow-up of 3.9 (5.9) years among insulin users and 179 events occurred over 4.6 (4.4) years among non-insulin users. The HR (95% CI) of dementia for insulin use vs non-insulin use was 1.68 (1.29-2.20) before adjustment and 1.39 (1.05-1.86) after multivariable adjustment, which was further attenuated to 1.14 (0.81-1.60) after IPTW weighting. Conclusions: Among persons with T2DM previously exposed to two non-insulin antihyperglycemic medications, no significant association was observed between insulin use and all-cause dementia.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".