Neuropsychological evaluation of pragmatics in a patient with acquired brain injury
Bibliographic record
Abstract
Studies in patients with brain injury have provided to clinical practice \na wide range of valuable language assessment tools and rehabilitation \nstrategies. In contrast, the ability to make a proper use of language \nadapted to a specific social and cultural context has been scarcely \nexplored in brain-damaged patients. Therefore, clinicians still lack \nspecific assessment batteries to diagnose pragmatic difficulties in \nthese patients. Given the importance of such disorders on their social \nand professional reinsertion, we aimed at studying the usefulness of \nthe Montréal Protocol for the Evaluation of Communication (MEC) in \norder to detect abnormal pragmatic capacities in a patient with a brain \ninjury, as compared to a control participant. In addition, we explored \nthe role of other cognitive processes, such as executive functions \nand social cognition on pragmatics. Results revealed that the MEC \nis a useful protocol to structure and guide the evaluation process \nof pragmatics, and it is sensitive to most of the symptoms observed \nat baseline. A partial dissociation between executive control and \npragmatics was evident in the presented case, along with an impaired \nability to recognize facial emotions, a difficulty that might explain \nsome of the symptoms observed at the pragmatic level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".