The role of injury severity in neurobehavioral outcome 3 months after traumatic brain injury.
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: To assess neurobehavioral outcome using the Neurobehavioral Rating Scale-Revised (NRS-R), an instrument with established specificity and validity in Traumatic Brain Injury (TBI) in a sample including the full spectrum of TBI severity 3 months after injury. METHOD: A cohort group of 102 subjects with mild TBI, 41 with moderate TBI, and 139 with severe TBI, from multiple academic trauma centers, were assessed using the NRS-R and the Glasgow Outcome Scale. RESULTS: Principal components analysis of the NRS-R resulted in a 3-factor model: (1) Cognitive, (2) Emotional, and (3) Hyperarousal. At 3 months, subjects with severe TBI show greater difficulties in cognitive and hyperarousal, but not emotional domains, than those with mild to moderate TBI. More than one third of subjects in all injury severity groups showed evidence of anxiety, depression, irritability, mental fatigability, and memory dysfunction. Scores on the NRS-R were related to outcome on the Glasgow Outcome Scale. CONCLUSIONS: Three months after injury, subjects with severe TBI have more dysfunction in cognitive and behavioral (but not emotional) domains than those with mild-to-moderate TBI. The NRS-R is a useful tool for assessing the full spectrum of neurobehavioral dysfunction at all ranges of TBI severity.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".