Secondary causes of elevated hemoglobin in patients undergoing molecular testing for suspected polycythemia vera in southwestern Ontario: a chart review
Bibliographic record
Abstract
Background: Molecular testing for JAK2 mutations is part of the standard diagnostic workup for patients with suspected polycythemia vera. We sought to characterize evolving practice patterns in the investigation of erythrocytosis and the prevalence of secondary causes, including use of medications such as sodium–glucose cotransporter-2 (SGLT2) inhibitors, among patients who underwent molecular testing. Methods: We reviewed charts of all consecutive patients investigated for erythrocytosis (hemoglobin > 160 g/L for women, > 165 g/L for men) with JAK2 testing between 2015 and 2021 at London Health Sciences Centre, a tertiary referral centre in Ontario, Canada, to assess changes in rates of JAK2 mutation positivity, average hemoglobin levels and the prevalence of secondary causes of erythrocytosis. Results: A total of 891 patients with erythrocytosis underwent JAK2 mutation testing with an increase in number of tests (particularly from 2017 to 2018), a decrease in the rate of JAK2 positivity and similar average hemoglobin levels over the study period. We observed a high proportion of patients with secondary causes of erythrocytosis, ranging from 59% to 74% over the study period, including medications associated with erythrocytosis, namely testosterone (6%–11%) and SGLT2 inhibitors (2%–19%). Stopping SGLT2 inhibitors was associated with a significant decrease in hemoglobin levels (mean −14.7 g/L, 95% confidence interval −18.9 to −10.5 g/L) compared with continuation. Interpretation: Use of SGLT2 inhibitors may be a common and underrecognized secondary cause of elevated hemoglobin levels in patients investigated for erythrocytosis. Our findings underscore the importance of a detailed medical history to support judicious use of molecular testing, in adherence with the current guideline on the investigation of erythrocytosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".