Differential impact of cerebral white matter changes, diabetes, hypertension and stroke on cognitive performance among non-disabled elderly. The LADIS study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Age related white matter changes (ARWMC) are frequent in non-demented old subjects and are associated with impaired cognitive function. Our aim was to study the influence of vascular risk factors and ARWMC on the neuropsychological performance of an independent elderly population, to see if vascular risk factors impair cognition in addition to the effects of ARWMC. METHODS: Independent subjects, aged 65-84 years, with any degree of ARWMC were assessed using a comprehensive neuropsychological battery including the Mini-Mental State Examination (MMSE), VADAS-Cog (Alzheimer's disease assessment scale) and the Stroop and Trail Making test. Vascular risk factors were recorded and ARWMC (measured by MRI) were graded into three classes. The impact of vascular risk factors and ARWMC on neuropsychological performance was assessed by linear regression analyses, with adjustment for age and education. RESULTS: 638 patients (74.1 (5) years old, 55% women) were included. Patients with severe ARWMC performed significantly worse on global tests of cognition, executive functions, speed and motor control, attention, naming and visuoconstructional praxis. Diabetes interfered with tests of executive function, attention, speed and motor control, memory and naming. Arterial hypertension and stroke influenced executive functions and attention. The effect of these vascular risk factors was independent of the severity of ARWMC, age and education. CONCLUSION: ARWMC is related to worse performance in executive function, attention and speed. Diabetes, hypertension and previous stroke influenced neuropsychological performance, independently of the severity of ARWMC, stressing the need to control vascular risk factors in order to prevent cognitive decline in the elderly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it