PREVENTABILITY OF DEMENTIA IN PRIMARY CARE SETTINGS
Bibliographic record
Abstract
Abstract Dementia is a long-term, chronic condition caused by a progressing physical damage in the brain. Evidence suggests that cardiovascular disease risk factors may contribute to the onset of dementia; however, the current literature on this association is inconsistent. To our knowledge, no study that has explored the occurrence of cardiovascular risk factors prior to a diagnosis of dementia using national primary care data in North America. We used electronic medical records from the Canadian Primary Care Sentinel Surveillance Network to create a Canadian cohort to conduct a retrospective analysis to (1) determine the number of incident diagnoses of dementia in primary care among community-dwelling seniors; (2) compare the risk of developing dementia in seniors (aged 65 and older) with and without modifiable cardiovascular risk factors. The cohort identified 21,628 patients who did not have a dementia diagnosis in 2008. During ten years of follow-up, 2,520 individuals developed dementia. The number of patients with dementia or cardiovascular risk factors increased slightly but steadily. Annually, the number of new cases of dementia increased from 0.5% in 2009 to 2.2% in 2017. Both Poisson regression and Cox’s proportional hazard model showed statistically significant relationships between hypertension, diabetes, dyslipidemia and obesity and dementia onset (p < 0.001), hazard ratio equals to 0.82, 1.11, 0.68 and 0.58, respectively. In other words, people with hypertension, dyslipidemia and obesity being managed in primary care are less likely to develop dementia. These findings support the hypothesis that good control over chronic diseases may benefit cognitive health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".