Alexithymia is associated with aggressive tendencies following traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: Aggressive behavior is a frequent legacy of traumatic brain injury (TBI). This study explores the question of how alexithymia, which is associated with deficits in social cognition and empathy, may predispose individuals to aggressive tendencies after head trauma. METHOD: A total of 47 individuals referred for routine neuropsychological assessment and advice on the management of long-term neuropsychological sequelae after TBI and 72 demographically matched controls completed the 20-Item Toronto Alexithymia Scale (TAS-20) and Buss Perry Aggression Questionnaire (BPAQ; self and proxy). RESULTS: The incidence of alexithymia and aggressive tendencies was significantly higher in the group with TBI. After controlling for covariates, alexithymia explained an additional 29% of variance in BPAQ total scores in the group with TBI and 11.1% in the control group. Of the three TAS-20 sub-scales, 'difficulty describing feelings' emerged as a consistent unique predictor of aggression scores. CONCLUSIONS: Higher levels of alexithymia are associated with greater aggressive tendencies post-TBI. The findings offer important theoretical and empirical insights into the prediction of aggression after TBI.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".