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Record W4402373693 · doi:10.1136/jnnp-2024-ehdn.330

J009 PROOF-HD: changes in q-motor and cuhdrs show an alignment of objective measures and clinical scales, and the capability of q-motor to predict long-term clinical outcomes

2024· article· en· W4402373693 on OpenAlexaff
Robin Schubert, Randal Hand, Kelly Chen, Y. Paul Goldberg, Henk Schuring, Michal Geva, Michael Hayden, Ralf Reilmann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTerm (time)Proof of conceptComputer sciencePhysical medicine and rehabilitationPsychologyMedicinePhysics

Abstract

fetched live from OpenAlex

Background Q-Motor measures lack rater-bias and revealed higher sensitivity and consistency than clinical scales in several trials, but little is known about the relationship of treatment responses between Q-Motor and clinical scales. Observational studies and clinical trials show correlation between longitudinal worsening in the objective measures of Q-Motor and worsening in clinical progression as assessed by cUHDRS, TFC, and TMS. Objective To assess correlations between the benefit of pridopidine in Q-Motor and measures of clinical progression, and to determine the predictive value of early benefits in Q-Motor towards later improvements in measures of clinical progression and function. Methods In PROOF-HD, the primary Q-Motor tapping endpoint ‘Inter-Onset-Interval’ (IOI) was analyzed in the ‘finger-tapping’ (digitomotography) and ‘pronate/supinate-hand-tapping’ (dysdiadochomotography) tasks. Correlations between changes in IOI and changes in cUHDRS and TFC at weeks 26, 52, 65 and 78 were calculated. A causal inference model adjusting for natural disease progression and baseline characteristics was applied to assess the relationship of these changes with pridopidine treatment. In addition, we assessed whether early changes to 26 weeks correlate with changes to later visits up to week 78. Results Changes in IOI and the cUHDRS were correlated at all visits in finger-tapping [r=-0.26–-0.41, p<0.0001] and pronate/supinate-tapping [r=-0.17–-0.30, p=0.0038–p<0.0001]. Early Q-Motor improvements at 26 weeks were correlated to benefits in cUHDRS at later visits, up to week 78 in finger-tapping [r=-0.17–-0.23, p=0.0048–p=0.00018] and pronate-supinate-tapping [r=-0.12–-0.19, p=0.036–r=0.0057]. The causal-interference-model confirmed these findings with significant relationships detected for treatment induced changes at all visits for both tasks [all p<0.0001]. The model suggests that every 10 msec benefit of pridopidine in finger-tapping is associated with an average of 0.1 less decline in cUHDRS (p<0.0001). Similar observations were seen with the TFC. Conclusion The correlation of observed changes in Q-Motor measures and cUHDRS and TFC suggest an alignment of objective measures and clinical scales in the assessment of treatment effects in PROOF-HD up to week 78. Furthermore, Q-Motor effects at 26 weeks are highly predictive of changes in cUHDRS and TFC at later visits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.054
GPT teacher head0.421
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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