Can Slow Personalized Titration Using C-Reactive Protein Monitoring Decrease the High Rates and Mortality of Clozapine-Associated Myocarditis Seen in Some Countries? A Call for Research
Bibliographic record
Abstract
PURPOSE/BACKGROUND: The hypothesis that slower personalized titration may prevent clozapine-associated myocarditis and decrease the disproportion incidence of 3% found in Australia was not described in a recent Australian article in this journal. METHODS: Six countries in addition to Australia have published information suggesting a similar incidence of clozapine-associated myocarditis. On September 19, 2023, PubMed searches were updated for articles from the United States, Korea, Japan, Canada, New Zealand, and Turkey. FINDINGS/RESULTS: An incidence of 3.5% (4/76) was found in a US hospital, but US experts were the first to propose that clozapine-associated myocarditis may be a hypersensitivity reaction associated with rapid titration and possibly preventable. Koreans and Japanese are of Asian ancestry and need lower minimum therapeutic doses for clozapine than patients of European ancestry. A 0.1% (2/1408) incidence of myocarditis during clozapine titration was found in a Korean hospital, but pneumonia incidence was 3.7% (52/1408). In 7 Japanese hospitals, 34% (37/110) of cases of clozapine-associated inflammation were found during faster titrations (based on the official Japanese titration) versus 13% (17/131) during slower titrations (based on the international titration guideline for average Asian patients). Recent limited studies from Canada, New Zealand, and Turkey suggest that slower personalized titration considering ancestry may help prevent clozapine-associated myocarditis. IMPLICATIONS/CONCLUSIONS: Other countries have very limited published data on clozapine-associated myocarditis. Based on a recent Australian case series and these non-Australian studies, the author proposes that Australia (and other countries) should use slow personalized titration for clozapine based on ancestry and c-reactive protein monitoring.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".