The Incidence of Tardive Dyskinesia in the Study of Pharmacotherapy for Psychotic Depression
Bibliographic record
Abstract
Tardive dyskinesia (TD) is a debilitating adverse effect associated with antipsychotic treatment. Older age and the presence of mood disorder have been identified as risk factors for the development of TD. Thus, we assessed the incidence of TD in younger and older patients with major depressive disorder with psychotic features who participated in a 12-week clinical trial comparing olanzapine plus sertraline versus olanzapine plus placebo. All subjects (n = 259) were assessed with the Abnormal Involuntary Movement Scale at baseline and after 4, 8, and 12 weeks of treatment (or at termination). We used 7 different published criteria to estimate the prevalence of TD at baseline and the incidence over the duration of the trial. We compared the incidence of TD in subjects 60 years or older and those younger than 60 years. The overall prevalence and incidence of TD varied almost 10-fold, depending on the criteria (prevalence range, 1.2%-8.9%; incidence range, 0.0%-5.9%). Tardive dyskinesia was observed as a clinical adverse event in only 1 subject (0.4%). Whereas older subjects had a higher prevalence of TD at baseline, the incidence in younger and older subjects did not differ significantly. The incidence of TD was relatively low in both younger and older patients with major depressive disorder with psychotic features treated acutely with olanzapine. However, the estimate of the risk of TD varies widely, depending on the criteria used to define 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".