A Preliminary Look Into the Clinical Evolution of Motor Speech Characteristics in Primary Progressive Apraxia of Speech in Québec French
Bibliographic record
Abstract
Purpose This study aimed to track changes in acoustical and perceptual features of motor speech in patients with phonetic and prosodic primary progressive apraxia of speech (PPAOS) in Québec French over an 18-month period. Method A prospective multiple-case series with multiple testing periods, including four participants with a diagnosis of PPAOS, was conducted. Participants were 0.5-4 years postonset of disease at baseline. They underwent comprehensive motor speech and language assessments and cognitive screening every 6 months for up to 18 months. Acoustical and perceptual analyses of motor speech were conducted. Results Results showed a considerable impairment in motor speech abilities for patients with PPAOS at all time points and a significant decrease in performance for almost all articulatory and prosodic measures over time. Passage reading and diadochokinesis seemed particularly promising for the tracking of changes in PPAOS motor speech characteristics and PPAOS classification. Quantifying length of speech runs made it possible to distinguish phonetic from prosodic PPAOS. Finally, the patients who evolved to phonetic PPAOS developed aphasia, and the two with prosodic PPAOS showed greater motor symptoms such as unequivocal dysarthria. Conclusion This study extends the growing literature on PPAOS and its subtypes by describing specific changes in articulatory and prosodic abilities over a period of at least 6 months, which are important for the diagnosis and management of PPAOS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".