Understanding the neuropsychiatric consequences associated with significant traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) can give rise to a variety of neuropsychiatric syndromes. The objective of this review is to describe the neurobiological mechanisms that have been proposed to underlie many of these post-TBI syndromes, explore the utility of various investigative modalities and review the mechanisms of treatment available for them. METHODS: Six authors reviewed PubMed and Ovid literature that addressed TBI in the context of the neuropsychiatric sequelae, evaluation and management. RESULTS: Depressed mood, anxiety, impulsive/aggressive behaviour, impaired memory and sleep disturbances are among the most prevalent sequelae of severe TBI. Delirium, while less common, can also result from TBI, predisposing individuals to other psychiatric conditions, while psychosis, usually presenting with atypical features, is relatively rare. The evaluation of the brain following TBI has often relied on traditional structural imaging which, according to recent studies, is less sensitive than chemical and functional neuroimaging. A variety of pharmacologic and non-pharmacologic treatments have been investigated with varying degrees of success in managing the spectrum of post-TBI psychiatric illnesses. CONCLUSIONS: Neuropsychiatric sequelae are common following TBI. Several of these syndromes are amenable to treatment. Further investigations are required, however, to better understand the mechanistic aetiology of these conditions and the effectiveness of various therapeutic modalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".