Longitudinal changes of <scp>SARA</scp> scale in Friedreich ataxia: Strong influence of baseline score and age at onset
Bibliographic record
Abstract
BACKGROUND: The Scale for Assessment and Rating of Ataxia (SARA) is widely used in different types of ataxias and has been chosen as the primary outcome measure in the European natural history study for Friedreich ataxia (FA). METHODS: To assess distribution and longitudinal changes of SARA scores and its single items, we analyzed SARA scores of 502 patients with typical-onset FA (<25 years) participating in the 4-year prospective European FA Consortium for Translational Studies (EFACTS). Pattern of disease progression was determined using linear mixed-effects regression models. The chosen statistical model was re-fitted in order to estimate parameters and predict disease progression. Median time-to-change and rate of score progression were estimated using the Kaplan-Meier method and weighted linear regression models, respectively. RESULTS: SARA score at study enrollment and age at onset were the major predictive factors of total score progression during the 4-year follow-up. To a less extent, age at evaluation also influenced the speed of SARA progression, while disease duration did not improve the prediction of the statistical model. Temporal dynamics of total SARA and items showed a great variability in the speed of score increase during disease progression. Gait item had the highest annual progression rate, with median time for one-point score increase of 1 to 2 years. INTERPRETATION: Analyses of statistical properties of SARA suggest a variable sensitivity of the scale at different disease stages, and provide important information for population selection and result interpretation in future clinical trials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".