L’apport critique de l’évaluation de la communication dans le diagnostic précoce de l’apraxie primaire progressive de la parole
Bibliographic record
Abstract
Primary progressive apraxia of speech (PPAoS) is a neurodegenerative syndrome characterized by speech apraxia at its onset; as it progresses, it often evolves into total mutism. Even though this syndrome is increasingly recognized, its early differential diagnostic is still complex. The objective of this study was to illustrate why a fine evaluation of speech and language is essential for the differential diagnosis of PPAoS. This longitudinal case study presents the progression of a PPAoS patient over a period of 5 years. Periodic neurological and speech-language assessments were carried out to follow the progression of neurological, memory, language and speech symptoms. The different diagnostic labels established over time were also reported. The evolution of the patient's communication profile was characterized by a preservation of language components and episodic memory, in parallel with a progressive deterioration of speech which gradually reduced intelligibility, and was associated with signs of spasticity, resulting in a complete anarthria. This case study sheds light upon the evolution of a patient with PPAoS. A better understanding of the clinical profile and progression of PPAoS is necessary in order to improve early diagnosis and adequate care for these patients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".