Exercise and Worsening of Extrapyramidal Symptoms during Treatment with Long-Acting Injectable Antipsychotics
Bibliographic record
Abstract
Second-generation antipsychotic medications are used to treat schizophrenia and a range of other psychotic disorders, although adverse effects, including cardiovascular and metabolic abnormalities and extrapyramidal symptoms, are often inevitable. Studies have shown that exercise, as an adjunct therapy, can be effective in reducing the core symptoms of schizophrenia as well as ameliorating intrinsic and antipsychotic-induced cardiometabolic abnormalities. However, it is noteworthy that exercise may need to be implemented with caution in some individuals receiving certain antipsychotic treatment regimens. We report here two cases of exercise-associated worsening of extrapyramidal symptoms in two individuals with schizoaffective disorder treated with a long-acting injectable antipsychotic medication over the course of a 12-week exercise program. This worsening of extrapyramidal symptoms can be attributed to an increase in blood flow to the site of injection during exercise, accelerating the rate of absorption and bioavailability of the antipsychotic medication and subsequently increasing dopamine D2 receptor blockade. When monitoring drug therapy for patients receiving long-acting injectable antipsychotic medications, pharmacists and other healthcare professionals need to consider exercise as a contributing factor for the emergence of extrapyramidal symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".