Hearing Loss and Dementia: A Population-Based Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Hearing loss (HL) is considered a potentially modifiable risk factor for dementia. We aimed to examine the relationship between HL and incident dementia diagnosis in a province-wide population-based cohort study with matched controls. METHODS: Administrative healthcare databases were linked to generate a cohort of patients who were aged ≥40 years at their first claimed hearing amplification devices (HAD) between April 2007 and March 2016 through the Assistive Devices Program (ADP) (257,285 with claims and 1,005,010 controls). The main outcome was incident dementia diagnosis, ascertained using validated algorithms. Dementia incidence was compared between cases and controls using Cox regression. Patient, disease, and other risk factors were examined. RESULTS: Dementia incidence rates (per 1,000 person-years) were 19.51 (95% confidence interval [CI]: 19.26-19.77) and 14.15 (95% CI: 14.04-14.26) for the ADP claimants and matched controls, respectively. In adjusted analyses, risk of dementia was higher in ADP claimants compared with controls (hazard ratio [HR]: 1.10 [95% CI: 1.09-1.12, p < 0.001]). Subgroup analyses showed a dose-response gradient, with risk of dementia higher among patients with bilateral HADs (HR: 1.12 [95% CI: 1.10-1.14, p < 0.001]), and an exposure-response gradient, with increasing risk over time from April 2007-March 2010 (HR: 1.03 [95% CI: 1.01-1.06, p = 0.014]), April 2010-March 2013 (HR: 1.12 [95% CI: 1.09-1.15, p < 0.001]), and April 2013-March 2016 (HR: 1.19 [95% CI: 1.16-1.23, p < 0.001]). CONCLUSION: In this population-based study, adults with HL had an increased risk of being diagnosed with dementia. Given the implications of HL on dementia risk, understanding the effect of hearing interventions merits further investigation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".