038 Cerebellar cognitive affective syndrome in ataxia-telangiectasia patients
Bibliographic record
Abstract
Background Cognitive function in Ataxia telangiectasia (AT) has been reported in few studies. The cer- ebellum is increasingly recognised as a pivotal structure in cognition and Cerebellar Cognitive Affective Syndrome (CCAS) has been observed in AT. Here, we report on cognitive studies in adult AT patients. Methods We performed cognitive assessments on an adult AT cohort and healthy volunteers. We assessed patients using the CCAS Scale, Mini Linguistic State Examination (MLSE) and the Test Your Memory (TYM) assessment. A nominated carer completed the Cambridge Behavioural Inventory Revised (CBI-R). AT patients were also assessed for severity of neurological symptoms using Scale for Assessment and Rating of Ataxia (SARA) and Inventory of Non-Ataxia Signs (INAS). Results Our interim analysis confirms Cerebellar Cognitive Affective Syndrome in the adult AT cohort. AT patients showed deficits primarily in tasks relying on executive function: semantic and phonemic fluency, category switching, digit span and affect. There were language deficits in motor speech and phonologi- cal errors greater than syntax or semantic errors. Conclusions Our large-scale cross-sectional study demonstrates the presence of cognitive dysfunction in AT affecting executive function, motor language function, and affect. We advocate routine cognitive assessment in AT patients as part of their clinical care.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".