NEUROLINGUISTIC AND ACOUSTIC STUDY OF LOGOPENIC PRIMARY PROGRESSIVE APHASIA IN ARABIC
Bibliographic record
Abstract
The primary progressive aphasia (PPA) or Mesulam syndrome is an isolated and progressive deterioration of language, usually due to progressive focal atrophy of the left peri sylvian regions. Given that very little data on PPA is available in non-Western languages in the literature, we describe the first case of logopenic PPA in Arabic. Neuropsychological, neuroimaging and linguistic protocol have been administered to the patient. The Neurolinguistic assessment was carried out with the Moroccan version of the Montreal-Toulouse linguistic exploration protocol, the apraxia of speech protocol, the Moroccan version of MLSE (Mini-Linguistic Status Examination); some subtests of the BDAE (Boston Diagnostic Aphasia Examination) while the computerized acoustic analysis was performed with Vocalab4 ™. The acoustic analysis showed mainly instability in pitch and amplitude. However articulatory disruptions are very mild in our case. There is a parallelism between spoken language which is marked by phonological paraphasias with a „pseudostuttering „and written language disorder which displays a phonological alexia, a severe acalculia and an agraphia. Our pa tient presents L-PPA subtype 1 on the logopenic spectrum. These results are consistent with the neuropsychological hypothesis of a dysfunction in phonological buffer reflecting the features of logopenic PPA. Furthermore, our case displayed atypical neurolinguistic patterns in comparison with other cases described in the European languages due to the Arabic specific linguistic structure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".