Validation of Automatic Diadochokinesis Tools for the Assessment of Dysarthria in Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Oral diadochokinesis (DDK) is a dysarthria assessment task. Automatic DDK analysis algorithms have mostly been validated in individuals with no to mild dysarthria and uniform syllable tasks. The goal of this study was to evaluate the performance of five DDK algorithms across dysarthria severity and syllable type. 282 recordings of /ba/, /pa/, and /ta/ from 145 participants with ALS were analyzed. The recordings were stratified into mild, moderate, or severe dysarthria groups. The number of syllables, DDK rate, and cycle-to-cycle temporal variability (cTV) were extracted. The absolute energy algorithm yielded the strongest agreement with manual analysis across all severity groups, but showed variation for /ta/ tasks. Absolute energy-based algorithms appeared to be the most robust for DDK analysis across dysarthria severity and syllable types, though limited against severe dysarthria and alveolar syllable contexts. This work can inform clinicians and researchers of the best tools to use while conducting DDK analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".