Quetiapine<scp>XR</scp>‐induced neutropenia: is a clozapine trial still possible for treatment‐resistant schizophrenia? A case report
Bibliographic record
Abstract
AIM: Our case report addresses the use of clozapine in patients who have a history of quetiapine XR-induced neutropenia. There are no current guidelines for this situation. METHODS: We present the case of a young woman treated with clozapine at a first-episode psychosis clinic after a moderate quetiapine XR-induced neutropenia (0,5-1,0 × 10(9) L(-1) ). RESULTS: The patient was successfully treated with clozapine and lithium, with less psychotic symptoms and a better level of functioning. The neutrophil count remained normal during the treatment period, which has been longer than a year. CONCLUSION: The outcome of this case supports the notion that clinicians could consider introducing clozapine in treatment-refractory patients who have a history of quetiapine XR-induced neutropenia, with close blood monitoring. Lithium co-administration may play a role in maintaining a normal neutrophil count.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".