[Characteristics of cognitive impairment in patients with leukoaraiosis].
Bibliographic record
Abstract
OBJECTIVE: To explore the characteristics of cognitive impairment in patients with leukoaraiosis (LA). METHODS: Forty-six LA patients and 38 age and gender-matched healthy subjects were recruited from the Department of Neurology, Beijing Chaoyang Hospital, Capital Medical University between September 2010 and March 2011. All participants underwent the neuropsychological tests recommended by the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards (NINDS/CSN). The were divided into 3 different groups (mild, moderate and severe) according to the Fazekas scale. The differences of neuropsychological performances were compared among 3 groups. RESULTS: The LA patients were associated with comprehensive cognitive function deficits, including MMSE (24.4 ± 3.2 vs 28.3 ± 1.2), MoCA (20.4 ± 3.0 vs 26.2 ± 0.8), digital span forward (5.7 ± 0.9 vs 6.8 ± 1.0), digital span backward (3.5 ± 0.7 vs 4.1 ± 0.7), Stroop-B (69 ± 13 vs 43 ± 5), Stroop-C (141 ± 42 vs 65 ± 10), trail making test-A (73 ± 15 vs 31 ± 7), trail making test-B (126 ± 18 vs 82 ± 6) and digit symbol test (25 ± 6 vs 37 ± 5, P < 0.05). However, there was no difference in the performance of verbal fluency (12.7 ± 2.5 vs 13.4 ± 2.5, P > 0.05). Correlation analysis showed that the severity of LA had a negative correlation with the performance of MoCA (r = -0.601, P = 0.002). CONCLUSIONS: The LA patients are closely correlated with cognitive impairments of attention, memory, executive function and information processing speed. It may be attributed to the frontal-subcortical circuitry dysfunction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".