Tardive Dyskinesia in the Era of Typical and Atypical Antipsychotics. Part 1: Pathophysiology and Mechanisms of Induction
Bibliographic record
Abstract
OBJECTIVE: Tardive dyskinesia (TD) is the principal adverse effect of long-term treatment with conventional antipsychotic agents. Several mechanisms may exist for this phenomenon. Mechanisms for the lower incidence of TD with atypical antipsychotics also remain to be fully understood. We undertook to explore and better understand these mechanisms. METHODS: We conducted a comprehensive review of TD pathophysiology literature from January 1, 1965, to January 31, 2004, using the terms tardive dyskinesia, neuroleptics, antipsychotics, pathophysiology, and mechanisms. Additional articles were obtained by searching the bibliographies of relevant references. Articles were considered if they contributed to the current understanding of the pathophysiology of TD. RESULTS: Current TD vulnerability models include genetic vulnerability, disease-related vulnerability, and decreased functional reserve. Mechanisms of TD induction include prolonged blockade of postsynaptic dopamine receptors, postsynaptic dopamine hypersensitivity, damage to striatal GABA interneurons, and damage of striatal cholinergic interneurons. Atypical antipsychotics may cause less TD because they have less impact on the basal ganglia and are less likely to cause postsynaptic dopamine hypersensitivity. CONCLUSION: Although the ultimate model for TD is not yet understood, it is plausible that several of these vulnerabilities and mechanisms act together to produce TD. The lower incidence of TD with atypical antipsychotics has helped to elucidate the,mechanisms of TD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".