Cognitive impairment in multiple system atrophy and spinocerebellar ataxias
Bibliographic record
Abstract
Abstract Background: Multiple system atrophy (MSA) and spinocerebellar ataxias (SCAs) share similar clinical symptoms. Therefore, it is challenging to differentiate MSA and SCAs according to clinical symptoms, especially in the early stage. Currently, the diagnosis still relies on auxiliary inspection and genetic testing. The difference in cognitive symptoms between MSA and SCAs has not been fully investigated. Hence, the aim of this study was to analyze the differences in cognitive impairment between MSA and SCAs. Methods: Five MSA patients and 5 patients with SCAs were recruited from the memory clinic of Xuanwu Hospital from March to September 2021. We collected detailed clinical information, imaging data, neuropsychological scales and genetic analysis of the patients. Then, we compared the differences in each cognitive domain between MSA and SCA patients. Results: Comparison of SCA and MSA patients revealed that MSA patients had lower scores on the Clinical Dementia Rating Scale (CDR). There were no statistically significant group difference in global cognitive functioning, as indicated by Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Conclusion: Both MSA and SCAs present with cognitive impairment, but MSA presents more obvious symptom severity.
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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) | 0.002 | 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".