Clinical observation on changes of cognitive function in patients with cerebral microbleeds
Bibliographic record
Abstract
Objective To investigate the relationship between the cerebral microbleeds (CMBs) and changes of cognitive function, and the possible mechanism of cognitive impairment caused by CMBs. Methods Sixty-eight micro-hemorrhage patients on susceptibility weighted imagine (SWI) sequences composed positive group, and sixty-eight patients selected without micro-hemorrhage in the SWI sequence and meeting the selection criteria as control group. At the same time, both two groups were assessed by MoCA and CDT scale inspection. Results CDT scores of CMBs group (2.00±0.88) were significantly lower than those of control group (3.76±0.53), and there was significantly different in the two groups (t=-3.27, P=0.00). At the same time, MoCA total scores and executive functions, naming, calculation, language, abstraction, recall scores of CMBs group were significantly lower than those of control group, and all of the groups were significantly different (t=-5.48, P=0.00; t=-4.36, P=0.00; t=-2.35, P=0.01; t=-2.49, P=0.02; t=-4.09, P=0.00; t=-4.63, P=0.00). CDT scores, MoCA total scores, executive functions, language, abstraction, memory scores between CMBs groups and control group were significantly different at all levels (P<0.05). Executive functions , languages and calculated inter-group of mild CMBs, moderate CMBs, severe CMBs were significantly different (P<0.05). The number of CMBs was negative correlation with total scores, executive function, language, and abstract (r=-0.675, P=0.000; r=-0.689, P=0.000; r=-0.536, P=0.000; r=-0.636, P=0.000). Conclusion The existence of CMBs and the number of CMBs are closely related to cognitive dysfunction. The more of CMBs, the more of obvious cognitive impairment. Key words: Cerebral microbleeds; Cognitive impairment; Montreal cognitive assessment scale; Clock drawing test
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".