Speech Movement Measures as Markers of Bulbar Disease in Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Purpose: The goal of this study was to identify the effects of amyotrophic lateral sclerosis (ALS) on tongue and jaw control, both cross-sectionally and longitudinally. The data were examined in the context of their utility as a diagnostic marker of bulbar disease. Method: Tongue and jaw movements were recorded cross-sectionally (n = 33 individuals with ALS, 13 controls) and longitudinally (n = 10 individuals with ALS) using a three-dimensional electromagnetic articulography system during the production of the sentence Buy Bobby a puppy. The movements were examined for evidence of changes in size, speed, and duration and with respect to disease severity and time in the study. Results: Maximum speed of tongue movements and movement durations were significantly different only at an advanced stage of bulbar ALS compared with the healthy control group. The longitudinal analysis revealed a reduction in tongue movement size and speed with time at early stages of disease, which was not seen cross-sectionally. As speaking rate declined, tongue movements decreased in maximum speed, whereas jaw movements increased in maximum speed. Conclusions: Longitudinal analyses of sentence-level kinematic data show their sensitivity to early bulbar impairment. A change in articulatory kinematics can serve as a useful diagnostic marker for bulbar ALS and to track bulbar disease progression in a clinical setting.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".