Clozapine clinical toolkit optimizes inpatient clozapine monitoring
Bibliographic record
Abstract
Introduction: Clozapine is the most effective antipsychotic in the management of treatment-resistant schizophrenia; however, its use is challenging due to the risk of severe adverse effects. Despite the risks associated with clozapine, there is no mandatory monitoring in Canada beyond hematologic testing for agranulocytosis surveillance. This study focuses on the development, implementation, and evaluation of a clozapine clinical toolkit (CTK) targeted at optimizing inpatient clozapine use. Methods: A comprehensive literature review was conducted to identify clozapine best practices, experts were consulted, and a comprehensive clozapine CTK was developed and implemented at a large Canadian tertiary hospital in December 2018. To evaluate the CTK, a retrospective chart review was conducted to assess for change in guideline-concordant monitoring pre- and post- CTK implementation. Patients were included if they were > 18 years of age and received clozapine during inpatient admission. Results were analyzed using descriptive and inferential statistics. Results: Among the charts reviewed, 185 and 113 admissions met the pre- and post-CTK inclusion criteria, respectively. Staff used the CTK in the care of 96% of clozapine patients post implementation, and its use resulted in improvements in guideline-concordant monitoring for agranulocytosis and myocarditis. Discussion: Implementation of the clozapine CTK increased the concordance of clozapine monitoring with best practice recommendations. Future research is necessary to assess the impact of the CTK on clinical outcomes and patient satisfaction.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".