Early prediction of language impairment following traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: This study investigated which factors collected early in the acute care setting (age, education, cerebral imaging, Glasgow Coma Scale score) would predict initial impairments of language comprehension and expression in patients with traumatic brain injury (TBI) of all severity. METHODS AND PROCEDURES: Results of language tests carried out during the patients' stay in an acute tertiary trauma centre were obtained. These tests measured performance in the areas of confrontation naming, auditory comprehension, semantic and letter category naming and comprehension of verbal absurdities. Data for the predictive variables were gathered by retrospective chart review. Stepwise multiple linear regressions were carried out on the predictive variables. MAIN OUTCOMES AND RESULTS: Education and TBI severity as measured with the GCS score were the most significant factors predicting language deficits in the acute care setting. CONCLUSIONS: These findings will serve to guide health care professionals in predicting prognosis for cognitive-communication deficits post-TBI and in planning for appropriate resources in speech-language pathology to meet these patients' needs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".