Assessing Aggression Following Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND: Every year, millions of people worldwide suffer traumatic brain injuries (TBIs). Aggressive behavior, a known psychological symptom following TBI, has been regarded as an obstacle toward rehabilitation. Having measures that accurately assess aggression during rehabilitation is critical toward proper evaluation. OBJECTIVE: To undertake a systematic review of the validated scales used to assess aggression in the postacute stage (≥3 months) after sustaining a TBI in the adult population. A comprehensive search was performed and studies meeting the inclusion criteria were reviewed in full. Quality and validity of supporting articles were assessed via the Downs and Black and QUADAS checklists along with their supporting statistics. RESULTS: A total of 1329 articles were reviewed from the literature. Thirty-two were reviewed in detail and 6 studies eventually passed the exclusion criteria. Of these, 6 neuropsychological scales were represented pertaining to the measurement of aggressive behavior; however, only 1 directly addressed the validity of their scale's aggression component. CONCLUSIONS: Further research is required to establish the validity of scales that specifically address aggression for use in the adult TBI population which could be used to support rehabilitation and social reintegration strategies.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".