Acute evaluation of conversational discourse skills in traumatic brain injury
Bibliographic record
Abstract
This study looked at performance on the conversational discourse checklist of the Protocole Montréal d'évaluation de la communication (D-MEC) in 195 adults with TBI of all severity hospitalized in a Level 1 Trauma Centre. To explore validity, results were compared to findings on tests of memory, mental flexibility, confrontation naming, semantic and letter category naming, verbal reasoning, and to scores on the Montreal Cognitive Assessment. The relationship to outcome as measured with the Disability Rating Scale (DRS), the Extended Glasgow Outcome Scale (GOS-E), length of stay, and discharge destinations was also determined. Patients with severe TBI performed significantly worse than mild and moderate groups (χ(2)(KW2df) = 24.435, p = .0001). The total D-MEC score correlated significantly with all cognitive and language measures (p < .05). It also had a significant moderate correlation with the DRS total score (r = -.6090, p < .0001) and the GOS-E score (r = .539, p < .0001), indicating that better performance on conversational discourse was associated with a lower disability rating and better global outcome. Finally, the total D-MEC score was significantly different between the discharge destination groups (F(3,90) = 20.19, p < .0001). Thus, early identification of conversational discourse impairment in acute care post-TBI was possible with the D-MEC and could allow for early intervention in speech-language pathology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".