Exploratory digital outcome measures of motor sign progression in Parkinson’s disease patients treated with prasinezumab
Bibliographic record
Abstract
Abstract Digital health technology (DHT) tools for Parkinson’s disease (PD) such as smartphones and wearable sensors enable remote and frequent measurement of motor signs which may facilitate research on novel treatments for PD. Here, we report the effect of the anti-alpha-synuclein monoclonal antibody prasinezumab on two-year motor sign progression in an early-stage PD population as measured with the Roche PD Mobile Application DHT. 316 early-stage PD participants of the phase 2 PASADENA study (NCT03100149) were randomized to treatment with placebo, 1500 mg, or 4500 mg prasinezumab for 52 weeks (Part 1); and placebo treated patients in Part 1 were re-randomized to 1500 mg or 4500 mg prasinezumab for the ensuing 52 weeks (Part 2, delayed-start group). Patients performed daily motor ‘active tests’ on provisioned study smartphones, carried the phone and wore a smartwatch throughout the day for passive monitoring, over two years. Change from baseline on 17 pre-specified individual sensor features and a summary Simple Sum score were analyzed with linear random slope and intercept models or mixed models for repeated measures, censoring patient data following the start of dopaminergic treatment. Adherence to active testing averaged 5.7 and 5.0 days/week in Parts 1 and 2, respectively. Bilateral speeded tapping variability and hand turning, U-turn speed, the power of passively monitored non-gait hand movements, and the Simple Sum score progressed less in prasinezumab-treated vs placebo at week 52. All findings except hand turning persisted at week 104. While the primary outcome for Part 1 PASADENA (MDS-UPDRS sum of Parts I, II and III) showed no significant difference from placebo, exploratory sensor-based outcome measures showed reduced motor sign progression in prasinezumab-treated individuals with early-stage PD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".