A.04 The classification of autosomal recessive cerebellar ataxias: a consensus statement from the society for research on the cerebellum and ataxias task force
Bibliographic record
Abstract
Background: There is currently no accepted classification of recessive cerebellar ataxias, a group of disorders characterized by important genetic heterogeneity and complex phenotypes. The objective of this task force was to build a consensus and develop a clinical and pathophysiological classification for recessive ataxias. Methods: The work of this task force was based on a scoping systematic review of the literature that identified recessive disorders characterized primarily by a cerebellar motor syndrome and cerebellar degeneration. The task force regrouped 12 international ataxia experts who decided on general orientation and specific issues. Results: We identified 59 disorders that are classified as primary recessive ataxias. For each of these disorders, we present geographical and ethnical specificities along with distinctive clinical and imagery features. The primary recessive ataxias were organized in a clinical and a pathophysiological classification, and we present a general clinical approach to the patient presenting with ataxia. We also identified a list of 48 complex multisystem disorders in which ataxia is a secondary feature. Conclusions: This classification is based on a scoping systematic review of the literature and results from a sconsensus among a panel of international experts. It promotes a unified understanding of recessive cerebellar disorders for clinicians and researchers.
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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 | 0.128 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".