Automatic Temporal Segmentation of Orofacial Assessment in Amytrophic Lateral Sclerosis
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurological disease that causes degeneration of lower and upper motor neurons in the brain, brainstem and spinal cord. Objective assessments of bulbar ALS include acoustic (audio) and kinematic (video) methods; the outcomes that are extracted as a result of these assessments are essential for improving early disease detection, and monitoring disease progression. There has been a rapid growth in the automatic - acoustic and kinematic - methods of speech assessment in ALS. Temporal segmentation (parsing) of orofacial assessment data is an important step underlying these assessments. Current clinical diagnosis obtain parsing either manually or in a semi-automatic manner which is time-consuming and labour-intensive. This thesis delivers insight into whether modern machine learning techniques can be applied to automatically and accurately parse ALS orofacial assessment data and lays the ground for automated and objective assessment tools for use in clinical and home settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".