Création d’un matériel en langue franco-française pour une réplication de la thérapie « personalized observation, execution and mental imagery (POEM) » ciblant l’anomie des verbes dans l’aphasie post-AVC et étude de ses effets auprès d’un participant avec aphasie
Bibliographic record
Abstract
Among the sequelae that can appear following a brain injury, aphasia is an acquired language disorder whose most frequent and persistent symptom is anomia. Despite the central pivotal place of the verb in the sentence, the literature mainly proposes studies targeting the recovery of noun anomia, but very few concern verb anomia. Rooted in the theory of mirror neurons and that of Embodied and Situated Cognition, the study by Durand et al. (2018) proposed a new protocol named POEM (Personalized Observation, Execution and Mental imagery) that targets verb recovery by combining for the first time three sensorimotor strategies: observation and action execution with mental imagery. Since the original material was created and calibrated from Quebec French, our study aimed at replicating the POEM protocol by creating a material adapted to the French language of France. The material was validated with a population of 95 people matched to the French population in terms of gender and age, using a video questionnaire. This calibration allowed us to select a total of 96 videos. We were then able to use this material by proposing POEM therapy to a patient with verb anomia in order to study the behavioral effects. Similar to the study by Durand et al. (2018), analysis of our results showed significant improvements for both treated and untreated items, indicating a generalization phenomenon. We were also able to observe improvements on other levels of processing such as noun and proposition processing. These data are discussed and compared with studies in the current literature. The results of the experimental study, although related to the analyses of the single patient’s results, invite further research on the related behavioral effects with a larger number of participants.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".