Moving towards meaningful measurement: Rasch analysis of the North Star Ambulatory Assessment in Duchenne muscular dystrophy
Bibliographic record
Abstract
AIM: Reliable measurement of disease progression and the effect of therapeutic interventions in Duchenne muscular dystrophy (DMD) require clinically meaningful and scientifically sound rating scales. Therefore, we need robust evidence to support such tools. The North Star Ambulatory Assessment (NSAA) is a promising, clinician-rated scale with potential uses spanning clinical practice and clinical trials. In this study, we used Rasch analysis to test its suitability in these roles as a measurement instrument. METHOD: NSAA data from 191 ambulant boys (mean age at assessment 7 y 8 mo, SD 2 y 4 mo; range 3 y 6 mo-15 y 5 mo) with a confirmed diagnosis of DMD were examined for psychometric properties including clinical meaning, targeting, response categories, model fit, reliability, dependency, stability, and raw to interval-level measurement. All analyses were performed using the Rasch Unidimensional Measurement Model. RESULTS: Overall, Rasch analysis supported the NSAA as being a reliable (high Person Separation Index of 0.91) and valid (good targeting, little misfit, no reversed thresholds) measure of ambulatory function in DMD. One item displayed misfit (lifts head, fit residual 6.9) and there was evidence for some local dependency (stand on right/left leg, climb and descend box step right/left leg, and hop on right/left leg, residual correlations >0.40), which we provide potential solutions for in future use of the NSAA. Importantly, our findings supported good clinical validity in that the hierarchy of items within the scale produced by the analyses was supported by clinical opinion, thus increasing the clinical interpretability of scale scores. INTERPRETATION: In general, Rasch analysis supported the NSAA as a psychometrically robust scale for use in DMD clinical research and trials. This study also demonstrates how Rasch analysis is a useful instrument to detect and understand the key measurement issues of rating scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".