Antipsychotic Induced Dopamine Supersensitivity Psychosis: A Comprehensive Review
Bibliographic record
Abstract
Chronic prescription of antipsychotics seems to lose its therapeutic benefits in the prevention of recurring psychotic symptoms. In many instances, the occurrence of relapse from initial remission is followed by an increase in dose of the prescribed antipsychotic. The current understanding of why this occurs is still in its infancy, but a controversial idea that has regained attention recently is the notion of iatrogenic dopamine supersensitivity. Studies on cell cultures and animal models have shown that long-term antipsychotic use is linked to both an upregulation of dopamine D2-receptors in the striatum and the emergence of enhanced receptor affinity to endogenous dopamine. These findings have been hypothesized to contribute to the phenomenon known as dopamine supersensitivity psychosis (DSP), which has been clinically typified as the foundation of rebound psychosis, drug tolerance, and tardive dyskinesia. The focus of this review is the update of evidence behind the classification of antipsychotic induced DSP and an investigation of its relationship to treatment resistance. Since antipsychotics are the foundation of illness management, a greater understanding of DSP and its prevention may greatly affect patient outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".