Integrating clozapine into PD services: Insights from a single neurosciences centre, North- East England
Bibliographic record
Abstract
Background Parkinson’s disease psychosis (PDP) is a common complication of PD, associated with high morbidity and mortality, and contributing to caregiver burnout. Managing PDP is challenging as most antipsychotics worsen motor function. Clozapine is a gold standard antipsychotic, effectively treats PDP without motor symptoms aggravation. However, its use is limited by agranulocytosis risk, necessitating frequent blood count monitoring. We provide an overview of Clozapine service experience in PASU (Parkinson’s Advanced Symptoms Unit); James cook university Hospital (JCUH). Results In PASU services, 1,500 Parkinson’s disease (PD) patients have been tracked, and Clozapine was integrated into the service in 2016. From 2016 to November 2023, 33 PD patients commenced Clozapine, averaging 2-6 patients annually. The average duration of PD diagnosis before starting Clozapine was 10.45 years, with doses ranging from 6.25mg to 125mg and a mean dose of 35.42mg; 97% began at 6.25mg. Visual hallucinations (91%) primarily led to Clozapine initiation, with 82% experiencing documented symptoms improvement. While 42% encountered side effects like drowsiness and constipation, only 12% discontinued Clozapine due to intolerable effects, and no cases of agranulocytosis were reported. Conclusion Drawing from our experience, Clozapine proves well-tolerated and effective antipsychotic in PDP, highlighting its excellent integration into PD services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".