Hypoglycaemia and the risk of dementia: a population-based cohort study using exposure density sampling
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown hypoglycaemia to be associated with an increased risk of dementia; however, there are several design challenges to consider. The objective of this study is to assess the association between hypoglycaemia and dementia while addressing these challenges using a lag period, exposure density sampling (EDS) and inverse probability of treatment weighting (IPTW). METHODS: This was a population-based cohort using data (1996-2018) from British Columbia, Canada. From a cohort of incident type 2 diabetes patients aged 40-70 years, we created a dynamic sub-cohort of hypoglycaemia-exposed (≥1 episode requiring hospitalization or a physician visit) and unexposed individuals using EDS, in which four unexposed individuals per one exposed were randomly selected into risk sets based on diabetes duration and age. Follow-up was until dementia diagnosis, death, emigration or 31 December 2018. Those diagnosed with dementia within 2 years of follow-up were censored. We adjusted for confounding using IPTW and estimated the hazard ratio (HR, 95% CI) of dementia using weighted conditional cause-specific hazards risk models with death as a competing risk. RESULTS: Among 13 970 patients with incident type 2 diabetes, 2794 experienced hypoglycaemia. There were 329 dementia events over a median (interquartile range: IQR) follow-up of 5.03 (5.7) years. IPTW resulted in well-balanced groups with weighted incidence rates (95% CI) of 4.59 (3.52, 5.98)/1000 person-years among exposed and 3.33 (2.58, 3.88)/1000 person-years among unexposed participants. The risk of dementia was higher among those with hypoglycaemia (HR, 1.83; 95% CI 1.31, 2.57). CONCLUSIONS: After addressing several methodological challenges, we showed that hypoglycaemia contributes to an increased risk of all-cause dementia among patients with type 2 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".