White Matter Disease Independently Predicts Progression from Mild Cognitive Impairment to Alzheimer’s Disease in a Clinic Cohort
Bibliographic record
Abstract
BACKGROUND: The contribution of vascular pathology to the rate of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) remains unclear. OBJECTIVE: To ascertain the relative roles of cerebral white matter disease and medial temporal atrophy (MTA) in predicting progression from MCI to AD. METHODS: MCI patients with baseline MRI and ≥18 months of longitudinal follow-up were evaluated. DSM-IV-TR criteria were used to diagnose conversion to dementia. MTA and white matter hyperintensity (WMH) were quantified using the Scheltens scale and modified Fazekas scale. RESULTS: Of a total of 171 MCI patients, 79 patients with baseline MRI and longitudinal follow-up were studied. Twenty-three MCI patients who progressed to dementia (MCI-P) were identified corresponding to a 19.4% annual risk of conversion. In MCI-P patients, the mean Mini-Mental State Examination and Montreal Cognitive Assessment decline was 1.3 and 2.9 points, respectively. MTA, periventricular WMH and deep subcortical WMH were significantly greater in the MCI-P cohort. WMH was found to predict MCI-P with an odds ratio of 7.69 (p = 0.03). CONCLUSION: MTA and deep subcortical WMH independently predict conversion from MCI to AD. Optimization of vascular risk factors among patients with MCI can potentially reduce the conversion from MCI to AD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".