A vascular risk factor index in relation to mortality and incident dementia
Bibliographic record
Abstract
To develop a method for quantifying risks of death and dementia in relation to vascular risk factors the Gothenburg H-70 1901-02 birth cohort was studied (n=380, was followed over 20 years, with 103 incident dementia cases). Separate vascular risk factor indices were calculated using 23 vascular risk factors to predict: (i) dementia-free-survival, and (ii) incident dementia derived from post hoc optimal separation of affected and unaffected cases. Classification of adverse outcomes (dementia/non-dementia; alive/dead) was assessed using receiver-operator characteristic (ROC) curves, and the area under the curve (AUC). Each index showed high separation between affected and unaffected cases. For dementia/non-dementia, the AUC was 0.74+/-0.02 for 10 year and 0.67+/-0.02 for 20 year; for death/survival, the AUC was 0.75+/-0.02 for 10 years and 0.79+/-0.03 for 20 years. Of note, few items were important in both indexes, and most showed reciprocal effects (e.g. decreased the risk of death but increased the risk of dementia). Our results suggest that vascular risk factor indexes can give robust estimates of dementia and life span prognoses in elderly people, but death and dementia have different risk profiles. This may be because of death being a competing risk for incident late-onset 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 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".