COVID-19 infection causes a reduction in neutrophil counts in patients taking clozapine
Bibliographic record
Abstract
Background: Monitoring of white cell counts during clozapine treatment leads to cessation of therapy if levels fall below predetermined values. Reductions in white cell counts, driven by lower levels of lymphocytes, have been observed with coronavirus disease 2019 (COVID-19). Neutropenia during COVID-19 has not been reported. We present data for 56 patients who were taking clozapine and had COVID-19. Methods: We included patients who were taking clozapine at the time they tested positive for COVID-19. We compared absolute neutrophil counts, lymphocyte counts and white cell counts between baseline and the first week of infection, and baseline and the second week of infection. Results: We observed reductions in absolute neutrophil counts (p = 0.005), lymphocyte counts (p = 0.003) and white cell counts (p < 0.001) between baseline and the first 7 days of COVID-19. All cell counts had returned to baseline levels by days 8 to 14. Six patients experienced neutropenia (absolute neutrophil counts < 2.0 × 109/L) and of those, 4 underwent mandatory cessation of clozapine. For 3 patients, clozapine treatment had been established for more than 6 months with no previous neutropenia, neutrophil levels returned to baseline within 2 weeks and no further neutropenia was observed on restarting treatment. Limitations: This was a retrospective chart review; larger cohorts are required. Clozapine plasma levels were largely not measured by clinicians. Conclusion: These data strongly suggest that mild neutropenia in the acute phase of COVID-19 in patients who are well established on clozapine is more likely to be a consequence of the virus than of clozapine treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".