Clozapine and Anemia
Bibliographic record
Abstract
OBJECTIVE: Clozapine's association with agranulocytosis led to the implementation of stringent and mandatory hematologic monitoring guidelines in most countries. Although other hematologic aberrations such as eosinophilia and neutropenia have been previously described, clozapine's impact on the erythroid lineage has not been studied. There is a suspicion that a higher rate of anemia is observed in patients receiving clozapine; therefore, we hypothesized that there would be a higher rate of anemia in patients receiving clozapine therapy. METHOD: All individuals initiated on clozapine at our center from 2009 to 2010 were recruited. Information on age, gender, medical comorbidities, and smoking status was extracted from the medical records. Data from complete blood counts over a 2-year follow-up period were extracted, with anemia defined as a hemoglobin value below 120 g/L for women and 130 g/L for men. Time to anemia event was calculated and Cox regression was employed to identify predictors of anemia. RESULTS: We found a high incidence of anemia in the first 2 years following clozapine initiation; of the 94 individuals (68 men, 26 women) recruited, 23 (24.5%) developed anemia. Higher baseline hemoglobin level (hazard ratio [HR] = 0.86, P = .002) and smoking status (HR = 0.21, P = .021) were identified as significant protective factors against anemia in men but not in women (HR = 0.92, P = .184, and HR = 0.52, P = .467 for baseline hemoglobin and smoking, respectively). CONCLUSIONS: Although smoking appears to lower the risk of anemia, we believe this is due to smoking's up-regulation of hemoglobin levels. Further studies are warranted in light of the present findings; for example, we cannot exclude the possibility that anemia was an epiphenomenon, characterizing instead a population with severe mental illness.
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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 | 0.003 |
| 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) | 0.003 | 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".