From Chlorpromazine to Clozapine—Antipsychotic Adverse Effects and the Clinician's Dilemma
Bibliographic record
Abstract
The pharmacotherapy of schizophrenia remains an ongoing challenge for researchers and clinicians alike. Current medications remain suboptimal to effectively treat this illness despite the recent surge of what are considered to be better antipsychotics: the atypicals. The atypicals cause fewer extrapyramidal symptoms and tardive dyskinesia, but there is growing concern regarding the significant long-term metabolic and cardiac adverse effects of these novel antipsychotics. There are differences among the atypicals in their propensity to produce these adverse effects, and clinicians should weigh the risk-benefit ratio for each drug with each individual patient. Diabetes, heart disease, obesity, and unhealthy lifestyle choices are on the rise in the general population, and individuals with chronic schizophrenia are even more at risk. The dilemma clinicians face in trying to avoid the neurological morbidity of the typicals (extrapyramidal side effects and tardive dyskinesia) is the risk of consequently exposing patients to both the morbidity and potential mortality of the atypicals (cardiovascular, endocrine, and metabolic adverse effects). The importance of baseline investigations and monitoring at regular intervals as well as identification of patients at risk for obesity, diabetes, and cardiovascular morbidity has become crucial. Informed decision making is essential for successful antipsychotic pharmacotherapy. For a condition, which often necessitates long-term pharmacotherapy, the importance of prevention and (or) minimization of morbidity and mortality related to adverse effects of such pharmacotherapy cannot be understated.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 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".