Microbleeds in fronto-subcortical circuits are predictive of dementia conversion in patients with vascular cognitive impairment but no dementia
Bibliographic record
Abstract
Cerebral small vessel disease (CSVD) is a common etiology of vascular cognitive impairment with no dementia (V-CIND). Studies have revealed that cerebral microbleeds (CMBs), a feature of CSVD, contribute to cognitive impairment. However, the association between CMBs and dementia conversion in individuals with V-CIND is still unclear. Here, we analyzed the predictive role of CMBs in the conversion from V-CIND to dementia in CSVD patients. We recruited and prospectively assessed 85 patients with CSVD and V-CIND. V-CIND was evaluated using a series of comprehensive neuropsychological scales, including the Chinese version of the Montreal Cognitive Assessment and the Clinical Dementia Rating. MRI assessments were used to quantify lacunar infarcts, white matter hyperintensities, CMBs, and medial temporal lobe atrophy. Eighty-two of the 85 patients completed the assessment for dementia conversion at a 1-year follow-up assessment. Multivariate logistic regression analyses were conducted to examine independent clinical and MRI variables associated with dementia conversion. Twenty-four patients (29.3%) had converted to dementia at the 1-year follow-up, and these individuals had significantly more CMBs in the fronto-subcortical circuits. Multivariate logistic regression analyses revealed that the patients with CMBs in the fronto-subcortical circuits (odds ratio = 4.4; 95% confidence interval: 1.602-12.081, P = 0.004) and 5 or more CMBs overall (odds ratio = 17.6, 95% confidence interval: 3.23-95.84, P = 0.001) had a significantly increased risk of dementia at the 1-year follow-up. These findings indicate that CMBs in the fronto-subcortical circuits may be predictive of dementia conversion in CSVD patients with V-CIND, and thus extend the clinical significance of CMBs. This trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR1800017077). Protocol version: 1.0.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".