Non-motor symptoms in patients with Spinocerebellar ataxia type 12
Bibliographic record
Abstract
Introduction: gene. This disease is classically characterized by action tremor, dysarthria, ataxia, and hyperreflexia. There are limited reports regarding the non-motor symptoms in patients with SCA 12. We investigated the frequency and factors associated with the selected non-motor symptoms in patients with SCA 12. Methods: Genetically diagnosed (CAG repeats > 43) and symptomatic cases of SCA 12 were included in the study. Motor severity was assessed using the Scale for the Assessment and Rating of Ataxia (SARA) and The Essential Tremor Rating Assessment Scale (TETRAS). Non-motor symptoms such as depression, autonomic function, and cognition were assessed using the Hamilton Depression Rating Scale (HAM-D), the Scales for Outcomes in Parkinson's Disease-Autonomic Dysfunction (SCOPA-AUT), and the Montreal Cognitive Assessment (MoCA), respectively. Results: The mean age of the cohort with 34 SCA12 patients was 64.87 years (standard deviation 6.844). In this study, 21 patients (61.76%) had either mild or moderate cognitive impairment, almost equally frequent in early and advanced cases. Similarly a number of patients demonstrated evidence of moderate and severe depression. The SARA score was strongly associated with the MoCA score, and CAG repeat length could independently predict the MoCA score in this cohort. Autonomic symptoms were commonly present, with the majority of the patients experiencing urinary symptoms. Discussion: Non-motor symptoms are common in SCA12 patients, even in the early stages of the condition. Timely detection and treatment of these symptoms may improve the therapeutic outcome and quality of life for SCA12 patients. The diverse symptoms of SCA12 perhaps indicate a widespread neurodegeneration beyond the cerebellum or its direct connections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".