Isolated, Subtle Neurological Abnormalities in Mild Cognitive Impairment Types
Bibliographic record
Abstract
BACKGROUND: Isolated, subtle neurological abnormalities (ISNA) are commonly seen in aging and have been related to cerebral small vessel disease (SVD) and subcortical atrophy in neurologically and cognitively healthy aging subjects. OBJECTIVE: To investigate the frequency of ISNA in different mild cognitive impairment (MCI) types and to evaluate for each MCI type, the cross-sectional relation between ISNA and white matter hyperintensities (WMH), lacunes, caudate atrophy, and ventricular enlargement. METHODS: One thousand two hundred fifty subjects with different MCI types were included in the analysis and underwent brain magnetic resonance imaging. WMHs were assessed through two visual rating scales. Lacunes were also rated. Atrophy of the caudate nuclei and ventricular enlargement were assessed through the bicaudate ratio (BCr) and the lateral ventricles to brain ratio (LVBr), respectively. Apolipoprotein E (APOE) genotypes were also assessed. The routine neurological examination was used to evaluate ISNAs that were clustered as central-based signs, cerebellar-based signs, and primitive reflexes. The items of Part-III of the Unified Parkinson's Disease Rating Scale were used to evaluate ISNAs that were clustered as mild parkinsonian signs. Associations of ISNAs with imaging findings were determined through logistic regression analysis. RESULTS: The ISNAs increase with the age and are present in all MCI types, particularly in those multiple domains, and carrying the APOE ϵ4 allele, and are associated with WMH, lacunes, BCr, and LVBr. CONCLUSION: This study demonstrates that cortical and subcortical vascular and atrophic processes contribute to ISNAs. Long prospective population-based studies are needed to disentangle the role of ISNAs in the conversion from MCI to 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".