A Spotlight on Friedreich Ataxia: Optimising the Patient Journey from Diagnosis to Disease Management
Bibliographic record
Abstract
This symposium was held on the first day of the European Academy of Neurology (EAN) Congress, with four main objectives: to raise awareness of Friedreich ataxia (FA) as a rare, progressive neurodegenerative disorder; to summarise the patient journey from identifying first symptoms in childhood and adolescence to reaching an accurate diagnosis; to discuss the burden of living with FA and highlight the benefit of improved communication and collaboration between members of the multidisciplinary team on reducing this burden on patients and their caregivers; and to summarise current management options within the field of FA and provide an overview of emerging therapies and active clinical trials. The symposium was chaired by Sylvia Boesch, a neurologist and senior staff member at the Medical University of Innsbruck, Austria, and Head of the Centre for Rare Movement Disorders, Innsbruck, Austria, who presented an overview of rare diseases in general and of FA. Mathieu Anheim, a neurologist at the Movement Disorders Unit, University Hospital of Strasbourg, France, followed with a description of the aetiology and symptomatology of FA. Lastly, Paola Giunti, a professorial research associate in the Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK, explained the best approach to FA management, including a summary of clinical trials for emerging therapies in FA.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".