The Relations of Self-Reported Aggression to Alexithymia, Depression, and Anxiety After Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: To compare self-reported aggression in people with and without traumatic brain injury (TBI) and examine the relations of aggression to alexithymia (poor emotional insight), depression, and anxiety. SETTING: Rehabilitation hospital. PARTICIPANTS: Forty-six adults with moderate to severe TBI who were at least 3 months postinjury; 49 healthy controls (HCs); groups were frequency matched for age and gender. DESIGN: Cross-sectional study using a quasi-experimental design. MAIN MEASURES: Aggression (Buss-Perry Aggression Questionnaire); alexithymia (Toronto Alexithymia Scale-20); depression (Patient Health Questionnaire-9); and trait anxiety (State-Trait Anxiety Inventory). RESULTS: Participants with TBI had significantly higher aggression scores than HCs. For participants with TBI, 34.2% of the adjusted variance of aggression was significantly explained by alexithymia, depression, and anxiety; alexithymia accounted for the largest unique portion of the variance in this model (16.2%). Alexithymia, depression, and anxiety explained 46% of the adjusted variance of aggression in HCs; in contrast to participants with TBI, depression was the largest unique contributor to aggression (15.9%). CONCLUSION: This was the first empirical study showing that poor emotional insight (alexithymia) significantly contributes to aggression after TBI. This relation, and the potential clinical implications it may have for the treatment of aggression, warrants further investigation.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".