Toxic Psychoses as Pharmacological Models of Schizophrenia
Bibliographic record
Abstract
To simplify the understanding of schizophrenia, psychiatry has invested important efforts into the modelling of the disease. For obvious ethical reasons, sleep deprivation and sensory deprivation models have been rejected in the last decades. Thus, the psychotic disorders induced by psychoactive substances remain the main heuristic models of schizophrenia. Most of the work in the pharmacological modelling of schizophrenia has occurred in the sixties and seventies. Still, some observations retain their interest: 1) lasting up to six months, the amphetamine psychosis remains the best model of the positive symptoms of paranoid schizophrenia; 2) hallucinogens such as LSD or psilocybine reproduce, at best, the psychotic phenomenology observed during the first episodes of schizophrenia; 3) anesthetic dissociatives (PCP & ketamine) faithfully reproduce thought disorder, as well as the frontal cognitive deficits and certain negative symptoms of schizophrenia; 4) cannabis reproduces with fidelity the depersonalization states observed among schizophrenics; 5) alcohol hallucinosis could be the best model of the patients hallucinations; 6) the psychotic disorder induced by muscarinic antagonists is not usually considered as a schizophrenia model, but its occurrence signals that cholinergic dysfunctions could be associated with the cognitive symptoms of the disease. As such, these observations go hand in hand with the common view of schizophrenia as a pathology of general cerebral disconnectivity that would affect a plurality of neurotransmitter systems. Keywords: toxic psychoses, psychoactive substances, schizophrenia, pharmacological models, neurotransmission
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".