The Incidence and Characteristics of Clozapine-Induced Fever in a Local Psychiatric Unit in Hong Kong
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence, characteristics, and predictors of clozapine-induced fever in a sample of patients in a local psychiatric unit. METHOD: A retrospective review of case notes of 227 inpatients newly started on clozapine from March 2003 to December 2006 was conducted. Demographic characteristics, presence of fever, investigations carried out, fever characteristics, and complications of fever were recorded and analyzed. Patients with clozapine-induced fever were compared with their fever-free counterparts on demographic and clinical factors. Multivariate logistic regression was performed to identify predictors of clozapine-induced fever. RESULTS: Thirty-one out of 227 patients (13.7%) developed clozapine-induced fever. The means for day of onset of clozapine-induced fever after clozapine initiation and duration of fever were 13.7 and 4.7 days, respectively. The mean highest body temperature was 38.8 degrees C. Fever resolved within 48 hours after clozapine discontinuation in 79% of the patients with clozapine-induced fever. One out of 7 patients (14.3%) had fever on re-challenge. Clozapine-induced fever was associated with rate of titration more than 50 mg/wk (OR 18.9; 95% CI 5.3 to 66.7; P < 0.01), concomitant use of valproate (OR 3.6; 95% CI 1.5 to 8.9; P = 0.01), and presence of physical illnesses (OR 3.2; 95% CI 1.2 to 8.3; P = 0.02). CONCLUSION: Clozapine-induced fever is common. Temporary withdrawal of clozapine may result in resolution of fever, and clozapine re-challenge may be considered after fever subsides. Slower rate of clozapine titration may be helpful in patients with underlying physical illness and concomitant valproate treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".