Early conversational discourse abilities following traumatic brain injury: An acute predictive study
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To date, little information is available regarding communication and conversational discourse proficiency post-traumatic brain injury (TBI) in the acute care phase. The main goal of this study was to examine how conversational discourse impairment following TBI predicts early outcome. Factors which influence conversational discourse performance were also explored. METHODS: The conversational discourse checklist of the Protocole Montréal d'évaluation de la communication (D-MEC) was administered in an acute tertiary care trauma centre to 195 adults within 3 weeks post-TBI. Outcome was measured with the Disability Rating Scale (DRS), the extended Glasgow Outcome Scale (GOS-E) and included discharge destinations from acute care. MAIN OUTCOMES AND RESULTS: Linear regression results showed that the D-MEC total score, age and initial GCS score accounted for 50% of the variation of the DRS scores. The DRS score was lower, signifying better outcome, when the total D-MEC score was higher, the subject was younger and when the initial GCS score was higher. Moreover, D-MEC performance significantly predicted the moderate and severe disability categories of the GOS-E and the probability of requiring rehabilitation (p < 0.05). CONCLUSION: These results provide additional information to guide healthcare professionals in predicting overall outcome acutely post-TBI.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".