Psychotic-Like Symptoms and the Temporal Lobe in Trauma-Related Disorders: Diagnosis, Treatment, and Assessment of Potential Malingering
Bibliographic record
Abstract
OBJECTIVE: To overview the phenomenology, etiology, assessment, and treatment of psychotic-like symptoms in trauma-related disorders focusing on the proposed role of temporal lobe dysfunction. METHOD: We describe the literature pertaining to (i) psychotic-like symptoms and temporal lobe dysfunction in trauma-related disorders and (ii) psychological testing profiles in trauma-related disorders. We define trauma-related disorders as borderline personality disorder, post-traumatic stress disorder, and the dissociative disorders. Our search terms were dissociative disorders, temporal lobe, trauma, post-traumatic stress disorder, borderline personality disorder, psychosis, and malingering. RESULTS: nonauditory content that may or may not relate to traumatic content. Child voices are highly suggestive of complex dissociative disorders. Critically, not only do these symptoms resemble those seen in temporal lobe epilepsy, but the temporal lobe is implicated in trauma-related disorders, thus providing a plausible neurobiological explanation. Despite such evidence, these symptoms are frequently considered atypical and misdiagnosed. Indeed, common structured psychological assessment tools categorize these symptoms as possible indicators of invalid testing profiles. CONCLUSION: Psychotic-like symptoms are common in trauma-related disorders, may be related to temporal lobe dysfunction, and are frequently misinterpreted. This may lead to ineffective treatment and inappropriate determinations of malingering in the forensic system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".