Insulin use and increased risk of mortality in type 2 diabetes: a cohort study
Bibliographic record
Abstract
AIM: To compare population-based rates of all-cause and cardiovascular (CV) mortality in newly treated patients with type 2 diabetes according to levels of insulin exposure. METHODS: Using the administrative databases of Saskatchewan Health, 12272 new users of oral antidiabetic therapy were identified between 1991 and 1996 and grouped according to cumulative insulin exposure based on total insulin dispensations per year: no exposure (reference group); low exposure (0 to <3); moderate exposure (3 to <12) and high exposure (> or =12). Time-varying multivariable Cox proportional hazards models were used to examine the relationship between insulin exposure and all-cause, CV-related and non-vascular mortality after adjustment for demographics, medications and comorbidities. RESULTS: Average age was 65 (s.d. 13.9) years, 45% were female, and mean follow-up was 5.1 (s.d. 2.2) years. In total, 1443 (12%) subjects started insulin, and 2681 (22%) deaths occurred. The highest mortality rates were in the high exposure group; 95 deaths/1000 person-years compared with 40 deaths/1000 person-years in the no exposure group [unadjusted hazard ratio (HR): 2.32; 95% confidence interval (CI): 1.96-2.73]. After adjustment, we observed a graded risk of mortality associated with increasing exposure to insulin: low exposure [adjusted HR (aHR): 1.75; 95% CI: 1.24-2.47], moderate exposure (aHR: 2.18; 1.82-2.60) and high exposure (aHR: 2.79; 2.36-3.30); p = 0.005 for trend. Analyses restricted to CV-related (p = 0.042 for trend) and non-vascular (p = 0.004 for trend) mortality showed virtually identical results. CONCLUSIONS: We observed a significant and graded association between mortality risk and insulin exposure level in an inception cohort of patients with type 2 diabetes that persisted despite multivariable adjustment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".