THE HIGH INCIDENCE ILLUSION: AKATHISIA WITH ARIPIPRAZOLE
Bibliographic record
Abstract
Background: Clinicians have voiced concerns over the possibly high frequency of akathesia that occur with aripiprazole use, however, the existing literature is not consistent with these observations.
 Aim: To compare the frequency of akathesia occuring with aripiprazole and risperidone.
 Method: A total of 60 patients were included in the study. Patients fulfilling the inclusion criteria were then randomly assigned into 2 groups of 30 patients each. One group is given Aripiprazole (10 mg) and the other is prescribed Risperidone (2mg). Patients of both groups were re-called on the 7th day after the start of the medication and were screened and calibrated for Akathisia using the Barnes Akathisia Rating Scale (BARS) with a cut off value of 2 or more on global assessment indicating the presence of Akathisia. The patients not having akathesia of the 7th day were put in a sub-group and were re-assessed for presence of akathesia on the 21st day following start of antipsychotic.
 Results: The akathesia assessment done on the 7th day revealed the presence of akathesia in two (6.67%) patients getting Aripiprazole 10 mg. One patient (3.34%) in the group receiving Risperidone 2 mg presented with akathesia of the 7th post treatment day. Furthermore, no patients in either of the sub-groups had akathisia when assessed on 21st post treatment day. One way ANOVA analysis gave p=0.895.
 Conclusion: The frequency of akathesia occuring with Aripiprazole is comparable to that with Risperidone and is considered low.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".