J011 Pridopidine demonstrates consistent improvements in Q-Motor measures and early benefits (26 weeks) in Q-Motor predict long-term changes in function and cUHDRS in PROOF-HD
Bibliographic record
Abstract
Background Q-Motor is an objective measure of motor function that is centrally read, lacks inter- and intra-rater variability, shows high reliability, and has minimal or no placebo response. Q-Motor shows consistent baseline correlations with core, independent clinical endpoints including, cUHDRS, TFC, TMS, SWR, SDMT, CAP-scores and brain-volume across numerous observational and clinical studies. Aims To assess the efficacy of pridopidine in Q-Motor measures in participants from the PROOF-HD study. Methods Q-Motor tests ‘finger tapping’ (FT, digitomotography) and ‘pronate/supinate hand tapping’ (PS, dysdiadochomotography) were collected and analyzed centrally. Results In all subjects, irrespective of antidopaminergic medication (ADM [neuroleptics and VMAT2 inhibitors]) use, Q-Motor measures favored pridopidine at all timepoints through 78 weeks. In subjects off ADMs, pridopidine’s benefit is greater in all Q-Motor measures assessed, including FT IOI mean at all visits (26-weeks, p<0.0001; 52-weeks, p=0.017; 65-weeks, p=0.013; and 78-weeks, p=0.003). Similar improvements are observed in PS Inter-Tap-Interval (ITI) mean. The robust benefits of pridopidine are maintained in subjects on lower doses of ADMs, as per the regulatory label for ADMs in the presence of a CYP2D6 inhibitor such as pridopidine. Early changes in Q-Motor (26 weeks) are highly predictive of long-term changes in numerous clinical measures, including TFC and cUHDRS (≥52 weeks). The predictability of Q-Motor measures are irrespective of ADM use. Conclusion Pridopidine demonstrated improvements in Q-Motor assessments at all timepoints irrespective of ADM use, with strongest and most significant effects in subjects off ADMs. Early benefits in Q-Motor were predictive of long-term benefits on key clinical outcome measures of disease progression.
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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) | 0.000 | 0.000 |
| 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) | 0.003 | 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".