Validation of Clinical Observations of Mastication in Persons with ALS
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurological disease that can result in difficulties with mastication leading to malnutrition, choking or aspiration, and reduced quality of life. When evaluating mastication, clinicians primarily observe spatial and temporal aspects of jaw motion. The reliability and validity of clinical observations for detecting jaw movement abnormalities is unknown. The purpose of this study is to determine the reliability and validity of clinician-based ratings of chewing performance in neuro-typical controls and persons with varying degrees of chewing impairments due to ALS. Adults chewed a solid food consistency while full-face video were recorded along with jaw kinematic data using a 3D optical motion capture system. Five experienced speech-language pathologists watched the videos and rated the spatial and temporal aspects of chewing performance. The jaw kinematic data served as the gold-standard for validating the clinicians' ratings. Results showed that the clinician-based rating of temporal aspects of chewing performance had strong inter-rater reliability and correlated well with comparable kinematic measures. In contrast, the reliability of rating the spatial and spatiotemporal aspects of chewing (i.e., range of motion of the jaw, consistency of the chewing pattern) was mixed. Specifically, ratings of range of motion were at best only moderately reliable. Ratings of chewing movement consistency were reliable but only weakly correlated with comparable measures of jaw kinematics. These findings suggest that clinician ratings of temporal aspects of chewing are appropriate for clinical use, whereas ratings of the spatial and spatiotemporal aspects of chewing may not be reliable or valid.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".