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Record W4412512208 · doi:10.1002/psp4.70081

Integrated Modeling of Digital‐Motor and Clinician‐Reported Outcomes Using Item Response Theory: Towards Powerful Trials for Rare Neurological Diseases

2025· article· en· W4412512208 on OpenAlexaff
Alzahra Hamdan, Andreas Traschütz, Lukas Beichert, Xiaomei Chen, Cynthia Gagnon, Bart P. van de Warrenburg, Filippo M. Santorelli, A. Nazlı Başak, Giulia Coarelli, Stephan Klebe, Rebecca Schüle, Andrew C. Hooker, Matthis Synofzik, Mats O. Karlsson

Bibliographic record

VenueCPT Pharmacometrics & Systems Pharmacology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de Sherbrooke
FundersElse Kröner-Fresenius-StiftungZonMwMinistero della SaluteDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsPhysical medicine and rehabilitationItem response theoryPsychologyMedicineNeuroscienceComputer scienceClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.220
metaresearch head score (Gemma)0.315
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.780
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2200.315
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0050.005
Open science0.0050.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.226
GPT teacher head0.446
Teacher spread0.220 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSimulation or modeling
DomainMethods
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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