188-LB: Association between Age at Diagnosis of Type 2 Diabetes and Hospitalization for Heart Failure (HHF)
Bibliographic record
Abstract
Background: The relation between age at T2D diagnosis and HHF is unclear. We conducted a population-based cohort study to examine the association between age at diagnosis of T2D and incident HHF in Ontario, Canada. Methods: Using administrative health databases, we identified people with new-onset T2D between April 1, 2005 and March 31, 2015. We matched each person to 3 other people without diabetes according to birth year and sex. We excluded any people with prior HHF. We used Cox proportional hazards models to estimate adjusted hazard ratios (HR) for the association between age at T2D diagnosis and incident HHF (followed up until March 31, 2020). Results: Among 743,053 individuals with T2D and 2,199,539 matches, 126,241 incident HHF events occurred (median follow-up 8.9 years). T2D was associated with a greater adjusted hazard of HHF at younger ages (e.g., HR at age 30 years: 5.71, 95% CI: 5.46-5.96; Figure: Model 1) than at older ages (e.g., HR at age 60 years: 2.51, 2.46-2.55). Additional adjustment for recognized mediators (hypertension, coronary artery disease, chronic kidney disease) marginally attenuated this relation (Figure: Model 2). Conclusion: Younger age at T2D diagnosis is independently associated with a disproportionately elevated risk of HHF relative to age-matched individuals without T2D. This relation may be substantially mediated by novel mechanisms that are inadequately understood. Disclosure C. Ke: Advisory Panel; Sanofi, Speaker's Bureau; AstraZeneca, Abbott. B. R. Shah: None. J. Echouffo tcheugui: None. Funding National Heart, Lung, and Blood Institute (K23HL153774 to J.E.T.); University of Toronto (to B.R.S.)
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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 | 0.000 |
| 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) | 0.007 | 0.001 |
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".