Minimally Detectable Change and Minimal Clinically Important Difference of a Decline in Sentence Intelligibility and Speaking Rate for Individuals With Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Purpose: The purpose of this study was to determine the minimally detectable change (MDC) and minimal clinically important difference (MCID) of a decline in speech sentence intelligibility and speaking rate for individuals with amyotrophic lateral sclerosis (ALS). We also examined how the MDC and MCID vary across severities of dysarthria. Method: One-hundred forty-seven patients with ALS and 49 healthy control subjects were selected from a larger, longitudinal study of bulbar decline in ALS, resulting in a total of 650 observations. Intelligibility and speaking rate in words per minute (WPM) were calculated using the Sentence Intelligibility Test (Yorkston, Beukelman, & Hakel, 2007), and the ALS Functional Rating Scale-Revised (Cedarbaum et al., 1999) was administered to capture patient perception of motor impairment. The MDC at the 95% confidence level was estimated using the following formula: MDC95 = 1.96 × √2 × SEM. For estimation of the MCID, receiver operating characteristic curves were generated, and area under the curve and optimal thresholds to maximize sensitivity and specificity were calculated. Results: The MDC for sentence intelligibility was 12.07%, and the MCID was 1.43%. The MDC for speaking rate was 36.57 WPM, and the MCID was 8.80 WPM. Both MDC and MCID estimates varied with severity of dysarthria. Conclusions: The findings suggest that declines greater than 12% sentence intelligibility and 37 WPM are required to be outside measurement error and that these estimates vary widely across dysarthria severities. The MDC and MCID metrics used in this study to detect real and clinically relevant change should be estimated for other measures of speech outcomes in intervention research.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".