Digital gait outcomes for ARSACS: discriminative, convergent and ecological validity in a multi-center study (PROSPAX)
Bibliographic record
Abstract
Abstract Background With treatment trials on the horizon, this study aimed to identify candidate digital-motor gait outcomes for Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS), capturable by wearable sensors with multi-center validity, and ideally also ecological validity during free walking outside laboratory settings. Methods Cross-sectional multi-center study (4 centers), with gait assessments in 36 subjects (18 ARSACS patients; 18 controls) using three body-worn sensors (Opal, APDM) in laboratory settings and free walking in public space. Sensor gait measures were analyzed for discriminative validity from controls, and for convergent (i.e. clinical and patient-relevance) validity by correlations with SPRS mobility (primary outcome) and SARA, SPRS and FARS-ADL (exploratory outcomes). Results Of 30 hypothesis-based digital gait measures, 14 measures discriminated ARSACS patients from controls with large effect sizes (|Cliff’s δ| > 0.8) in laboratory settings, with strongest discrimination by measures of spatiotemporal variability Lateral Step Deviation (δ=0.98), SPcmp (δ=0.94) and Swing CV (δ=0.93). Large correlations with the SPRS mobility were observed for Swing CV (Spearman’s ρ = 0.84), Speed (ρ=-0.63) and Harmonic Ratio V (ρ=-0.62). During supervised free walking in public space, 11/30 gait measures discriminated ARSACS from controls with large effect sizes. Large correlations with SPRS mobility were here observed for Swing CV (ρ=0.78) and Speed (ρ=-0.69), without reductions in effect sizes compared to lab settings. Conclusion We identified a promising set of digital-motor candidate gait outcomes for ARSACS, applicable in multi-center settings, correlating with patient-relevant health aspects, and with high validity also outside lab settings, thus simulating real-life walking with higher ecological validity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".