Detecting Bulbar Motor Involvement in ALS: Comparing speech and chewing tasks
Bibliographic record
Abstract
Purpose: To compare two different tasks and kinematic measures in terms of their ability to detect Amyotrophic lateral sclerosis (ALS) and differences in ALS severity in order to establish potential candidate markers of bulbar decline.Method: We tracked jaw kinematics during speech and chewing to determine which is more affected by bulbar motor deterioration, based on measures of maximum speed and articulatory working space. Data were collected from 31 individuals diagnosed with ALS and 17 neurologically intact controls.Result: (1) Both sentence and chewing tasks were effective in distinguishing between the groups of individuals with ALS and controls, (2) jaw maximum speed for both chewing and speech was a more sensitive marker for bulbar dysfunction than articulatory working space, (3) the sentence task distinguished between ALS subgroups stratified by severity and (4) distinct jaw kinematic differences existed between chewing and sentence tasks. More specifically, movement speed for speech decreased with severity while movement speed for chewing increased with disease severity.Conclusion: The findings from the current investigation suggest that measures of jaw movement speed during chewing and sentence tasks are affected by bulbar deterioration, and jaw speed during a sentence task may serve as a candidate marker of bulbar disease onset and severity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".