No difference in dementia prediction between apolipoprotein E4 and the ischemic score
Bibliographic record
Abstract
INTRODUCTION: Few biomarkers exist for early detection of vascular cognitive impairment. We examined whether the Hachinski Ischemic Scale (HIS) can predict dementia in elderly. METHODS: We leveraged data of the Canadian Study of Health and Aging. First, we examined the association of HIS with incident dementia. Next, we compared HIS to apolipoprotein E (APOE ɛ4) in prediction of dementia. We trained the HIS and APOE ɛ4 models in the training dataset and used the trained models for dementia prediction in the validation dataset. RESULTS: A higher HIS level was associated with a higher odds of dementia (odds ratio = 1.64, 95% confidence interval [CI]: 1.41 to 1.90, P < .001). Dementia discrimination of the HIS model was not different from the APOE ɛ4 model (area under the curve difference = 0.002, 95% CI: -0.024 to 0.029, P = .857). The calibration of the HIS model was 13.7 (P = .091) and of the APOE ɛ4 model was 13.3 (P = .100). DISCUSSION: HIS may be used as a simple, inexpensive test to identify older adults at risk of developing 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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".