Integrated Modeling of Digital‐Motor and Clinician‐Reported Outcomes Using Item Response Theory: Towards Powerful Trials for Rare Neurological Diseases
Bibliographic record
Abstract
Robust and highly sensitive outcomes are crucial for small trials in rare diseases. Combining different outcome types might improve sensitivity to identify disease severity and progression, yet innovative methodologies are scarce. Here we develop an Item Response Theory framework that allows integrated modeling of both continuous and categorical outcomes (ccIRT). With degenerative ataxias, a group of rare neurological coordination diseases, as a showcase, we developed a ccIRT model integrating two ataxia outcome types: a clinician-reported outcome (Scale for the Assessment and Rating of Ataxia; SARA; categorical data) and digital-motor outcomes for gait and limb coordination (continuous data). The ccIRT model leveraged data from 331 assessments from a natural history study for spastic ataxias. The model describes SARA items and digital-motor outcomes data as functions of a common underlying ataxia severity construct, evaluating 9 gait and 17 limb coordination digital-motor measures for their ability to add to SARA in estimating individual ataxia severity levels. Based on our proposed workflow for assessing digital-motor outcomes in ccIRT models, the final model selected three digital gait and three limb coordination measures, reducing average uncertainty in ataxia severity estimates by 49% (10% SD) compared to the SARA-only IRT model. Trial simulations showed a 49% and 61% reduction in sample sizes needed to detect disease-modifying effects in two genotypes. Overall, our ccIRT framework for combining multiple outcome domains, even of different variable types, facilitates a more precise estimation of disease severity and a higher power, which is particularly relevant for rare diseases with inherently small and short trials. Trial Registration: ClinicalTrials.gov: NCT04297891.
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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 | 0.220 | 0.315 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".