Integrating the Findings from Boundary Sciences for Development of the DSM/ICD Classifications
Bibliographic record
Abstract
Introduction Temperament and mental illnesses are considered to be varying degrees along the same continuum of imbalance in the neurophysiological regulation of behavior. Mental disorders are linked to specific patterns in the relationships between neurotransmitters and between brain structures. Similar links were found for temperament traits. Development of DSM and ICD classifications might benefit therefore from an integration between psychiatry, functional neurochemistry and differential psychology. Objectives To describe the neurochemical systems underlying mental disorders and temperament traits in healthy adults. Methods Findings in neurochemistry, neuropsychology, differential psychology and psychopathology are compared to the traits described in various temperament models. This analysis is summarized in the perspective of the neurochemical functional ensemble of temperament (FET) model. Results Neurochemical correlates for 12 main dynamical aspects of behavior are presented as a systemic framework that follows a universal functional structure of human actions described in kinesiology, neuroanatomy, neurochemistry and clinical neuropsychology. The role of monoamine systems (serotonin, dopamine, noradrenalin), acetylcholine, GABA/glutamate, neuropeptide and opioid receptor systems are linked to regulation of specific dynamical properties of behavior in a systematic way. Several insights for the structure of the classification of mental disorders from the perspective of the FET model are proposed. Conclusions An integration of research in neurochemistry and psychopathology of behavior with differential psychology based on healthy samples can bring new insights for future versions of DSM and ICD classifications of mental disorders. Such integration does not follow either dimensionality or categorical approach but instead is based on functional ecology of human behavior. Disclosure of interest The author has not supplied his 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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".