Évaluation de l’appétence musicale et des compétences musicales de la population aphasique en phase subaiguë post-AVC
Bibliographic record
Abstract
Strokes can cause language problems in many people. Due to its central origin, aphasia is frequently associated with other disorders including musical disorders. Amusia, or musical disorder, is a congenital or acquired neurological disorder that affects musical perception and production to varying degrees. When providing speech therapy for aphasic patients, rehabilitation based on musical tools can be offered to them. However, few studies have specifically examined the musical skills of people with aphasia, and even fewer their musical appetite. However, this information is important for the success of rehabilitation approaches that rely on musical tools. Based on these facts, we evaluated the musical skills of production (singing “Happy Birthday”) and reception (Montreal Battery for Evaluation of Aphasia) as well as musical appetite (Barcelona Music Reward Questionnaire and Music Anhedonia Questionnaire-Collateral) of 25 people with post-stroke aphasia in the subacute phase. The results revealed a high prevalence of amusia (32%) and musical anhedonia (loss of musical pleasure) (36%) in the study population as well as a reduction in musical appetite. after a stroke. In addition, we observed a statistical trend towards a moderate correlation between the achievement of musical skills and the achievement of musical pleasure. No correlation between these musical deficits and independent variables such as age and sex, for example, could be statistically demonstrated. Future larger-scale studies are possible to confirm these initial results and study the link between acquired language and musical deficits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".