Cognitive dysfunction in elderly patients with acute cerebral infarction and leukoaraiosis
Bibliographic record
Abstract
Objective To investigate the cognitive dysfunction in elderly patients with acute cerebral infarction( ACI) and leukoaraiosis( LA). Methods One hundred twenty- eight ACI patients were collected and divided into ACI + LA group( n = 76) and ACI group( n = 52) in accordance with whether they had LA. All patients were classified into four subtypes of ACI according to Oxfordshire Community Stroke Project( OCSP) classification. Beijing Version of the Montreal Cognitive Assessment( MoCA) was used to determine cognitive impairment and the patients were further divided into cognitive impairment group( n = 57) and non- cognitive impairment group( n = 71). LA was graded according to Aharon- Ptretz standard. Relevant clinical data was collected and statistically analyzed. Results An average score of MoCA in ACI + LA group( 20. 78 ± 4. 71) was significantly lower than the ACI group( 26. 45 ± 5. 02). Comparison of MoCA score in different grades of LA found that MoCA score in ACI + LA group( grade 1 to 4) was significantly lower than ACI group( grade 0). Comparison between each subgroup found that MoCA score in grade 1 patients was significantly higher than grade 3 and grade 4 patients. Univariate analysis showed there were significant differences in infarction location and LA between cognitive and non- cognitive impairment group. Multivariate analysis showed both infarction location and LA affected cognitive dysfunction. Conclusion Infarction location and LA are independent risk factors for cognitive dysfunction,with infarction location more important than LA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".