Bibliographic record
Abstract
Abstract White matter damage may play an important role in the pathogenesis of vascular dementia. White matter abnormalities are easily visualized as white matter high‐intensity lesions (WML) on T2‐weighted magnetic resonance images. The extent of WML may be an indicator of cognitive impairment, in particular, impairment related to frontal lobe dysfunction. However, it is unclear whether the extent of WML is an independent predictor of cognitive impairment. In patients with extensive WML, atrophy of the corpus callosum may be an important predictor of global cognitive impairment. We investigated the relationship between the extent of WML and callosal size with cognitive function in patients who had been diagnosed with lacunar stroke or no specific neurological disease. Multivariate analysis showed that only callosal size and age were significant independent predictors of mini‐mental state examination scores (a measure of global cognitive function), whereas only the extent of WML was an independent predictor of the score on the verbal fluency task (a measure of frontal lobe function). Callosal atrophy may be an important predictor of global cognitive impairment in patients with WML, whereas the extent of WML per se may be related to impairment of frontal lobe function independent of callosal atrophy. White matter high‐intensity lesions with callosal atrophy may indicate a severe form of white matter damage with axonal loss, the degree of which may determine the severity of global cognitive impairment. Our longitudinal study revealed an association between progression of WML and vascular risk factor status during follow up in patients with initially mild WML. Early detection of WML without callosal atrophy at a stage of subtle cognitive impairment and slowing the progression of WML to a severe form with callosal atrophy might prevent the development of 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".