A Case Report of Complex Clozapine Initiation Despite Contraindications
Bibliographic record
Abstract
INTRODUCTION: Clozapine is well known for its unique efficacy in treatment-resistant schizophrenia and to reduce violent behaviour. Unfortunately, life-threatening adverse reactions including ileus, myocarditis and agranulocytosis can hinder its use. In this context, some clinicians may be reluctant to initiate clozapine in patients who are prone to these adverse drug reactions. OBJECTIVES: To describe a complex clozapine initiation despite the presence of serious adverse effects and contraindications. The management of these adverse events, using effective multidisciplinary team leadership strategies, will also be described. METHODS: A case report will be presented. The challenges faced while using clozapine and strategies implemented to pursue the use of this medication will be described. RESULTS: A young black man with severe first episode psychosis was admitted to the early intervention outpatient clinic in Québec, Canada. Multiple aggression and critically disorganized behaviour prompted patient transfer to a specialized long-term care unit. Given the severity of the resistant disease and after a shared decision-making process with the family, clozapine was introduced despite ethnic neutropenia (down to 0,2 X 10(9)/L) and idiopathic cerebral lesions. Both gave rise to multiple concerns. A specific hematological surveillance protocol was designed. Facing multiple severe neutropenia episodes, the use of prophylactic granulocyte colony-stimulating factor (300 mcg SC weekly) was added after literature review and a favourable consult of both pharmacist and hematologist. Cardiac enzyme elevation also requested specialized investigation and follow-up. Specialized educators, social workers, and nursing all needed to be deeply involved in the treatment process and team coordination requested strong team building capacities. After 6 months, the patient is now taking clozapine 325 mg daily and his symptomatology has sufficiently reduced to allow hospital leave. The patient is now engaged in his recovery process. CONCLUSIONS: Using an evidence-based approach, promoting expertise from multiple healthcare professionals, and allowing a substantial amount of time to develop team cohesion were all crucial elements of this success story. DISCLOSURE OF INTEREST: None Declared
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".