Bibliographic record
Abstract
Antipsychotics are typically used for the treatment of schizophrenia, bipolar disorder, and recently, treatment resistant major depressive disorder. A significant, and very concerning, side effect present with first generation antipsychotics is extrapyramidal symptoms, which are disorders of movement. With the advent of atypical antipsychotics, also known as second-generation antipsychotics, these symptoms are purported to be much less frequent and pronounced than they were with the first generation medications. Numerous hypotheses have been proposed as to why atypical antipsychotics produce fewer extrapyramidal symptoms compared to first generation antipsychotics, which this paper will review. Unfortunately, despite the fact that atypicals have reduced extrapyramidal symptoms in those taking antipsychotics, extrapyramidal symptoms are still an unpleasant and potentially dangerous side effect, which can be difficult to detect, and difficult, or even impossible, to treat. Additionally, atypical antipsychotics result in other potentially very serious side effects, specifically and most commonly, metabolic syndrome, which can decrease life expectancy significantly. However, metabolic syndrome, unlike extrapyramidal symptoms, may be preventable in highly motivated and well-supported patients. Thus, this paper concludes that the benefits of the atypical antipsychotics (reduced extrapyramidal symptoms) outweigh the potential risks for the majority of patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".