Tardive Dyskinesia and Long-Acting Injectable Antipsychotics
Bibliographic record
Abstract
This study compared the reporting frequency of tardive dyskinesia (TD) between long-acting injectable antipsychotics (LAI-APs) and the equivalent oral antipsychotics (O-APs), LAI first-generation antipsychotics (LAI-FGAs) and LAI second-generation antipsychotics (LAI-SGAs), and individual LAI-APs. The Japanese Adverse Drug Event Report was used in this study, and data were obtained from April 2004 to February 2021. Patients who received LAI-APs available in Japan (LAI haloperidol, LAI fluphenazine, LAI aripiprazole, LAI risperidone, and LAI paliperidone) or the equivalent O-APs were included in this study. We calculated the adjusted reporting odds ratios (aRORs) to compare the reporting frequency of TD. A total of 8,425 patients were included in the study. TD was reported significantly less frequently with LAI paliperidone than with oral paliperidone (aROR [95% confidence interval (CI)] = 0.13 [0.05-0.36]). Other LAI-APs were associated with a numerically lower reporting frequency of TD than the equivalent oral SGAs. The reporting frequency of TD associated with LAI-SGAs was significantly lower than that of LAI-FGAs (aROR [95% CI] = 0.18 [0.08-0.43]). All LAI-SGAs were significantly associated with a lower reporting frequency of TD than that of LAI fluphenazine (aROR [95% CI]: LAI aripiprazole, 0.11 [0.04-0.35]; LAI risperidone, 0.09 [0.03-0.32]; LAI paliperidone, 0.02 [0.005-0.09]). and LAI haloperidol, 8.58 [1.85-39.72]). LAI fluphenazine was significantly associated with a higher reporting frequency of TD than LAI haloperidol (aROR [95% CI] = 8.58 [1.85-39.72]). The reporting frequency of TD associated with LAI paliperidone was significantly lower than that with LAI aripiprazole (aROR [95% CI] = 0.18 [0.05-0.73]). Compared to O-APs, LAI-APs, particularly LAI-SGAs, may be associated with a lower risk 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".