Atypical Antipsychotics: New Directions and New Challenges in the Treatment of Schizophrenia
Bibliographic record
Abstract
"Atypical" antipsychotics represent a new generation of antipsychotics with a significantly lower incidence of extrapyramidal side effects (EPS), as well as little or no effect on prolactin elevation. These advantages constitute a major improvement in the treatment of patients with schizophrenia. The exact mechanisms that make these drugs atypical is not clear. However, a preferential action on serotonin 5-HT2 or D4 receptors, or a more rapid dissociation from the dopamine D2 receptor, may account for atypicality. Although the atypical antipsychotics have overcome EPS, other side effects such as weight gain and impaired glucose tolerance/lipid abnormalities have come to the fore. Thus, the challenges are far from over. The current atypicals are much more effective against the psychosis of schizophrenia than against the other, more enduring aspects of this disorder, e.g. negative symptoms and cognitive dysfunction. At present, the atypicals use a "pharmacological shotgun" strategy to treat aspects of the disease in all patients. A more sophisticated and perhaps effective approach to schizophrenia may lie in independently targeting the pathophysiological mechanisms of each clinical dimension (i.e. positive, negative, cognitive, and affective) with more selective drugs that can be combined and individually titrated to the needs of each patient.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".