Results:The inter-rater agreements for MOAKS data (Figures 2.A.1, 2.B.1 & 2.C.1) were 1) for raw grades: 0.69 kappa and 0.78 balanced accuracy (patella), 0.71 kappa and 0.83 balanced accuracy (femur), 0.72 kappa and 0.79 balanced accuracy (tibia), 2) for 2 classes: 0.68 kappa and 0.86 balanced accuracy (patella), 0.69 kappa and 0.88 balanced accuracy (femur), 0.69 kappa and 0.87 balanced accuracy (tibia) and 3) for 3 classes: 0.70 kappa and 0.85 balanced accuracy (patella), 0.69 kappa and 0.86 balanced accuracy (femur), and 0.73 kappa and 0.87 balanced accuracy (tibia). Harmonized MOAKS and mWORMS with a heuristic approach (Figures 2.A.2, 2.B.2 & 2.C.2) achieved 1) for 2 classes, a balanced accuracy of: 0.83 for patella, 0.79 for femur, and 0.71 for tibia, and 2) for 3 classes: 0.79 balanced accuracy for no lesion class, 0.72 for PT class, and 0.87 for FT class (patella), 0.84 balanced accuracy for no lesion class, 0.74 for PT class, and 0.85 for FT class (femur), and 0.82 balanced accuracy for no lesion class, 0.68 for PT class, and 0.88 for FT class (tibia). Harmonized MOAKS and mWORMS with a machine learning approach (Figures 2.A.3, 2.B.3 & 2.C.3) achieved 1) for 2 classes, a balanced accuracy of: 0.80 for patella, 0.79 for femur, 0.77 for tibia, and 2) for 3 classes: 0.82 balanced accuracy for no lesion class, 0.72 for PT class, and 0.84 for FT class (patella), 0.81 balanced accuracy for no lesion class, 0.66 for PT class, and 0.8 for FT class (femur) and 0.8 balanced accuracy for no lesion class, 0.62 for PT class, and 0.8 for FT class (tibia). Conclusions: Harmonization of SQ assessments combines data graded with different scaling systems into a single homogenized source. A simplified two or three class scale reduces variability and improves the reliability of annotations for deep learning models. Given sufficiently balanced data, a machine learning approach allows for a more balanced sensitivity and specificity, and is less susceptible to inserting bias in labeled data. Harmonization performance is similar to inter-rater variability, and that machine learning techniques can be used to harmonize multiple data sources.
Background The Clinch Token Transfer Test (C3t) is a bi-manual coin transfer task that incorporates cognitive tasks to add complexity. This study explored the concurrent and convergent validity of the C3t as a simple, objective assessment of impairment that is reflective of disease severity in Huntington’s, that is not reliant on clinical expertise for administration. Methods One-hundred-and-five participants presenting with pre-manifest (n = 16) or manifest (TFC-Stage-1 n = 39; TFC-Stage-2 n = 43; TFC-Stage-3 n = 7) Huntington’s disease completed the Unified Huntington’s Disease Rating Scale and the C3t at baseline. Of these, thirty-three were followed up after 12 months. Regression was used to estimate baseline individual and composite clinical scores (including cognitive, motor, and functional ability) using baseline C3t scores. Correlations between C3t and clinical scores were assessed using Spearman’s R and visually inspected in relation to disease severity using scatterplots. Effect size over 12 months provided an indication of longitudinal behaviour of the C3t in relation to clinical measures. Results Baseline C3t scores predicted baseline clinical scores to within 9–13% accuracy, being associated with individual and composite clinical scores. Changes in C3t scores over 12 months were small ($$\Omega$$ Ω ≤ 0.15) and mirrored the change in clinical scores. Conclusion The C3t demonstrates promise as a simple, easy to administer, objective outcome measure capable of predicting impairment that is reflective of Huntington’s disease severity and offers a viable solution to support remote clinical monitoring. It may also offer utility as a screening tool for recruitment to clinical trials given preliminary indications of association with the prognostic index normed for Huntington’s disease.
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