| Sumario: | Purpose: The minimal detectable change (MDC) of questionnaire ordinal scores (MDCord) is the smallest score difference exceeding the measurement error. Being ordinal, it suffers from flaws, successfully addressed by the Rasch analysis (RA) and its robust interval measures. However, RA measures struggle to become established, likely because scores are more straightforward. This study aims to derive the MDCord of two upper limb measures, the Fugl-Meyer Assessment-Upper Limb (FMA-UL) and the Functional Assessment Test for Upper Limb (FAST-UL), from the MDC of their RA interval measures (MDCint). Methods: Two methodologies are applied. The first, based on a sensitivity and specificity analysis, defines the MDCord as the score difference with the highest accuracy in identifying a change per the MDCint. The second derives the MDCord from RA strata, another MDCint formulation. Various computations are tested, possibly resulting in slightly different MDCord. Results: The MDCord of the FMA-UL from sensitivity and specificity analysis was 8 and 4–5 for the FAST-UL. Using RA strata, the FMA-UL MDCord was 8–10, and that of the FAST-UL was 4–5. Conclusions: An easy-to-use MDCord has been provided for the FMA-UL and the FAST-UL, which, anchored to the RA MDCint, benefits from its robust measurement properties. Clinical Trials Registry: NA. IMPLICATIONS FOR REHABILITATION: The Fugl-Meyer Assessment-Upper Limb (FMA-UL) and the Functional Assessment Test for Upper Limb (FAST-UL) provide total scores of upper limb dexterity, with the FMA-UL being a recognised criterion standard for assessment in central paresis. The Minimal Detectable Change (MDC) values for the FMA-UL and FAST-UL total scores were derived from Rasch analysis indices, yielding a range of 8–10 points for the former and 4–5 points for the latter. The MDCs for the FMA-UL and FAST-UL derived in this study allow clinicians and clinical researchers to precisely evaluate how an individual patient's upper limb impairment has changed over time. For psychometricians, this paper introduces new methods for obtaining simple, ordinal MDCs anchored on the Rasch analysis framework.
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