Development and validation of a disease severity index for ataxia of Charlevoix-Saguenay
Bibliographic record
Abstract
Objective To develop a disease-specific severity index for adults with autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) (DSI-ARSACS) that considers the 3 components (pyramidal, cerebellar, neuropathic) of the disease, and to document its content validity, internal consistency, and construct validity. Methods The Beta DSI-ARSACS (17 items) was developed based on literature review and expert inputs and then administered to 26 participants. Items reduction was based on Cronbach α and desirable criteria. Performance measures were administered to assess the construct validity of the final version of the DSI-ARSACS. Results The final DSI-ARSACS have 8 items that can be easily performed during usual medical follow-up. The mean score was 19.6 ± 8.1 (range 6.0–35.5) and the Cronbach α was 0.912. The DSI-ARSACS score increased with disease stage and age (p ≤ 0.001) and was closely correlated with other measures assessing similar construct (9-Hole Peg Test, 10-Meter Walk Test, Scale for the Assessment and Rating of Ataxia, Berg Balance Scale, Barthel Index) (rs = 0.75–0.95, p < 0.01). A moderate but not significant correlation was found with the 6-Minute Walk test (rs = −0.611, p = 0.108). Conclusions The DSI-ARSACS is a valid measure of disease severity for the adult ARSACS population that is able to distinguish between patients with different clinical profiles. Further documentation of metrologic properties is necessary, but these first results are promising.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".