Documenting manifestations and impacts of autosomal recessive spastic ataxia of Charlevoix–Saguenay to develop patient-reported outcome
Bibliographic record
Abstract
BACKGROUND: Autosomal recessive cerebellar ataxias (ARCA) are a group of rare inherited disorders characterized by degeneration or abnormal development of the cerebellum. Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is one of the most prevalent in Europe. OBJECTIVES: The aim of this study is to provide a better understanding of the manifestations and impacts of ARSACS. METHODS: A systematic review of the literature was conducted, followed by a qualitative study using semistructured interviews and discussion groups to obtain the experience of people affected. RESULTS: According to the PROMIS framework, the results show manifestations and impacts in three components of health: physical, mental, and social. Fatigue and struggles with balance and dexterity are the physical manifestations of the disease most often cited by participants. Negative affects such as frustration and depression are among the mental health impacts with some loss in cognitive abilities. Social health is the least documented component; nonetheless, people with the disease report significant impacts in terms of social relationships, activities and work. CONCLUSIONS: These findings shed new light on the experience of people with recessive ataxia and identify key aspects to assess to improve their overall health.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".