Bibliographic record
Abstract
BACKGROUND: Hypertension's role in dementia is regarded as modest, because it often overlooks stroke's role. We assessed the dementia risk attributed to hypertension, considering stroke as an intermediary factor, given that stroke is associated with an increased dementia risk and that over half of strokes are linked to hypertension. METHOD: We recalculated hazard ratios and the dementia risk proportion attributable to hypertension using weighted population attributable fractions (accounting for overlapping risk factors), incorporating the dementia risk associated with stroke, the stroke risk linked to hypertension, and the prevalence of hypertension. RESULT: Our analysis reveals that hypertension increases the risk of stroke fivefold and the risk of dementia eightfold. This corresponds to approximately 16.6% of dementia cases being preventable through hypertension control only (Figure 1). By addressing hypertension effectively, the overall potential for dementia prevention could exceed 59.6%. CONCLUSION: Our recalculations of current evidence suggest that hypertension's role in dementia could be eight times higher than previous estimates when accounting for the role of stroke, underscoring the urgent need for enhanced global prevention strategies. Hypertension's high lifetime risk, widespread prevalence, frequent underdiagnosis, and inadequate management offer the single greatest opportunity for delaying, allaying, or preventing stroke, heart disease, and dementia.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".