Cerebellar cognitive affective syndrome in patients with spinocerebellar ataxia type 10
Bibliographic record
Abstract
BACKGROUND: Spinocerebellar ataxia type 10 (SCA10) is an autosomal dominant cerebellar ataxia, characterized by epilepsy, ataxic symptoms, and cognitive impairments linked to Cerebellar Cognitive Affective Syndrome (CCAS). The Cerebellar Cognitive Affective Syndrome Scale (CCAS-S) has been developed to identify CCAS across various cerebellar pathologies. OBJECTIVE: To determine whether patients with SCA10 exhibit CCAS using the CCAS-S, and to compare its effectiveness with the Montreal Cognitive Assessment (MoCA). A secondary objective was to evaluate the effect of demographic and clinical data on CCAS-S performance. METHOD: Fifteen patients with SCA10 and fifteen matched controls underwent assessments using the CCAS-S, the MoCA, the Scale for the Assessment and Rating of Ataxia (SARA), and the Center for Epidemiologic Studies Depression Scale (CES-D). Diagnostic accuracy was analyzed using ROC curve analysis, comparing total and subcategory scores between groups. Demographic and clinical data were examined for relations with CCAS-S scores. RESULTS: The CCAS-S effectively distinguished cognitive impairments in SCA10 patients, showing satisfactory sensitivity and specificity (AUC of 0.83). Although no significant differences were found in the AUCs between CCAS-S and MoCA (p = 0.45), the CCAS-S demonstrated a significantly larger effect size in the comparison between patients and control group (d = 2.33). Cognitive performance was poorer in patients than in controls (p = < 0.001), with depressive symptoms and age having a significant impact on CCAS-S outcomes. CONCLUSIONS: Patients with the SCA10 mutation exhibit CCAS. Besides the significant cognitive impairment, also detected by MoCA, the CCAS-S score was significantly affected by indicators of depressive mood and age, highlighting the importance of considering these variables during outcome analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".