External Validation of the JAKPOT Score for Diagnosing JAK2-Positive Erythrocytosis: A Retrospective Cohort Study
Bibliographic record
Abstract
Background/Objectives: Erythrocytosis is a common laboratory abnormality affecting approximately 4% of males and 0.4% of females. The JAKPOT score was recently developed to differentiate primary from secondary erythrocytosis without molecular testing. JAKPOT+ patients meet any of the following criteria: erythrocytes > 6.45 × 1012/L, platelets > 350 × 109/L, or neutrophils > 6.2 × 109/L. We aimed to validate this score and identify predictors of JAK2-positive erythrocytosis in a retrospective cohort. Methods: We identified 213 patients (50 female, mean age 57 years) with undifferentiated erythrocytosis, serum erythropoietin (EPO) and JAK2 molecular testing (V617F or exon 12) at a tertiary care center in Hamilton, Canada, between 2017 and 2022. Charts were manually reviewed for laboratory data, comorbidities, demographics, and medications. We evaluated the diagnostic accuracy of EPO, JAKPOT, and a combination of low EPO and JAKPOT (EPO-JAKPOT) for predicting JAK2 mutant erythrocytosis. Multivariate logistic regression analysis was performed to detect predictors of JAK2 mutant erythrocytosis. Results: Forty patients (19%) had JAK2 mutations. Older age (p < 0.01), higher platelet count (p < 0.01), and lower EPO (p < 0.01) were associated with JAK2 mutant erythrocytosis in a multivariate analysis. JAKPOT+ status had a sensitivity of 0.88 (95% CI, 0.73–0.94). Combining low EPO or JAKPOT+ status into a new score (EPO-JAKPOT) increased sensitivity to 0.95 (95% CI, 0.83–0.98). Restricting JAK2 testing to only EPO-JAKPOT+ patients would have led to 55% fewer molecular tests in our cohort. Conclusions: The EPO-JAKPOT score shows promise in excluding JAK2 mutant erythrocytosis without molecular testing, but further prospective validation is warranted.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".