Benefits of the functional ensemble of temperament framework in assessment of mental disorders: Examples
Bibliographic record
Abstract
Introduction An integration between psychiatry, neurochemistry and differential psychology gives an evidence-based framework for the diagnosis of mental illness rooted both in modern neurophysiology and clinical observations. Objectives To investigate whether, a neurochemical model of temperament might (FET) provide a better discrimination between major depression (MD), anxiety (GAD), co-morbid depression and anxiety and delusional disorders than existing emotionality-based temperament models. Methods Three studies compared the profiles on temperament and personality disorder inventories in patients who were diagnosed and treated for named disorders across four adult age groups (17–24, 25–45, 46–65, 66–84). Results The FET distinguished between MD and GAD in line with the DSM descriptors and showed significant differences for the traits of motor endurance and motor tempo (much lower values in MD), and neuroticism (much higher value in GAD). The results showed benefits of differentiation between physical and social types of fatigue as a symptom of MD and that high impulsivity and low plasticity can be also considered symptoms differentiating between mental disorders. Moreover, high sociability appeared as a symptom associated with high dominance–mania tendencies. The FET framework appeared to be sensitive to age and sex differences: higher anxiety and anti-social symptoms appeared to be more prominent in the younger age (unlike depression symptoms), and declined with age. Conclusions This study suggest the utility of using a functional approach to both taxonomy of temperament and classification of mental disorders and the benefits of systemic differentiating between 12 functional aspects of behavior, with special attention to non-emotionality-related aspects of behavior. Disclosure of interest The author has not supplied his/her declaration of competing interest.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".