Diagnosis, management, and outcomes of drug-induced erythrocytosis: a systematic review
Bibliographic record
Abstract
ABSTRACT: Secondary erythrocytosis refers to an elevation in hemoglobin or hematocrit due to elevated serum erythropoietin levels. Medications including testosterone and sodium-glucose cotransporter-2 (SGLT-2) inhibitors are increasingly recognized as causes of secondary erythrocytosis. We conducted a systematic review to inform the clinical management of drug-induced erythrocytosis. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search in MEDLINE, EMBASE, CENTRAL (all via Ovid), and Google Scholar. Of the 2036 articles screened for eligibility, 45 studies were included in our review, with 35 studies on testosterone and other androgen use, 5 studies on SGLT-2 inhibitors, 3 studies on antiangiogenic tyrosine kinase inhibitors (TKIs), 1 study on erythropoiesis-stimulating agents, and 1 study on a treatment regimen for multidrug-resistant tuberculosis. Cisgender and transgender men on prescription testosterone had erythrocytosis rates of up to 66.7%, with intramuscular formulations, higher doses, and older age associated with increased risk of erythrocytosis. Up to 2.7% of men on testosterone therapy developed thromboembolic events. Among individuals on SGLT-2 inhibitors, erythrocytosis rates ranged from 2.1% to 22%, with those who discontinued therapy demonstrating improvement or resolution of erythrocytosis. Thromboembolic events were reported in up to 10% of these individuals. Antiangiogenic TKIs were studied in patients with cancer, with erythrocytosis developing in up to 43.5% of patients. Drug-induced erythrocytosis is a heterogeneous condition for which there is no clear consensus among clinicians about its diagnosis and management. We offer recommendations for clinical practice within the scope of this systematic review, although further research is required.
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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) | 0.002 | 0.000 |
| 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".