Brain functional integration: an epidemiologic study on stress-producing dissociative phenomena
Bibliographic record
Abstract
Dissociative phenomena are common among psychiatric patients; the presence of these symptoms can worsen the prognosis, increasing the severity of their clinical conditions and exposing them to increased risk of suicidal behavior. Personality disorders as long duration stressful experiences may support the development of dissociative phenomena. In 933 psychiatric outpatients consecutively recruited, presence of dissociative phenomena was identified with the Dissociative Experience Scale (DES). Dissociative phenomena were significantly more severe in the group of people with mental disorders and/or personality disorders. All psychopathologic traits detected with the symptom checklist-90-revised had a significant correlation with the total score on the DES. Using total DES score as the dependent variable, a linear regression model was constructed. Mental and personality disorders which were associated with greater severity of dissociative phenomena on analysis of variance were included as predictors; scores from the nine scales of symptom checklist-90-revised, significantly correlated to total DES score, were used as covariates. The model consisted of seven explanatory variables (four factors and three covariates) explaining 82% of variance. The four significant factors were the presence of borderline and narcissistic personality disorder, substance abuse disorders and psychotic disorders. Significant covariates were psychopathologic traits of anger, psychoticism and obsessiveness. This study, confirming Janet's theory, explains that, mental disorders and psychopathologic experiences of patients can configure the chronic stress condition that produces functional damage to the adaptive executive system. The symptoms of dissociative depersonalization/derealization and dissociative amnesia can be explained, in large part, through their current and previous psychopathologic experiences.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".