Lobar microbleeds are associated with cognitive impairment in patients with lacunar infarction
Bibliographic record
Abstract
Associations between cognitive decline and cerebral microbleeds (CMBs) have received increasing attention. An association between CMB distribution (deep or lobar) and cognitive decline has been reported, but these findings are controversial. We investigated the association between magnetic resonance imaging (MRI) findings, including CMBs, and cognitive function in patients with first-ever lacunar infarction. We retrospectively included consecutive patients admitted with first-ever lacunar infarction identified by MRI from July 1, 2011, to December 31, 2018. We excluded patients diagnosed with dementia, including strategic single-infarct dementia, before or after the onset of stroke. The Mini-Mental State Examination (MMSE) was performed within 3 days of admission. We searched the records of 273 patients (age 72.0 ± 11.2 years, 95 females). The median MMSE score was 27 (interquartile range 25.5-29). In a univariate analysis, the MMSE score was associated with age, body mass index (BMI), education, dyslipidemia, chronic kidney disease (CKD), periventricular hyperintensity, medial temporal atrophy, lobar CMBs, and mixed CMBs (p < 0.20). The lacunar infarction location was not associated with the MMSE score. In a multivariate analysis of these factors, lobar CMBs (p < 0.001) and mixed CMBs (p = 0.008) were independently associated with the MMSE score. Lobar CMBs were associated with cognitive impairment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".