Alexithymia and aggression in patients with antisocial personality disorder
Bibliographic record
Abstract
Objective:Alexithymic characteristics have been observed in antisocial personality disorder (APD). On the other hand, aggression is a particular problem commonly observed in personality disorders, especially in APD. We investigated the alexithymic features and aggression levels in outpatients diagnosed with APD in a military hospital setting.Methods:71 male subjects diagnosed with APD and 81 sex and age matched normal subjects with no known medical or psychiatric disorder were assessed with an assessment battery using a sociodemographic data form, APD section of SCID-II, the Toronto Alexithymia Scale (TAS)-20 items, and Aggression Questionnaire.Results:The subjects with APD have showed significantly higher rates of unemployment, lower educational and socioeconomic status. The APD group also displayed significantly higher scores on alexithymia and aggression than control group. APD subjects with higher scores of aggression revealed significantly higher scores of alexithymia.Conclusion:Use of action to express emotions, a commonly observed feature of APD, was once considered to be a part of alexithymia. The subjects with APD may have less developed cognitive skills which lead to a failure in communicating their feelings. This may result in immature methods of communicating distress. Our study indicate that alexithymia may contribute to aggressive behavior in patients with APD. To draw a more definitive conclusion on this issue, larger community based studies that compares APD subjects with sociodemographically matched patient control groups are necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".