Phenotypical differences and thrombosis rates in secondary erythrocytosis versus polycythemia vera
Bibliographic record
Abstract
Erythrocytosis is a common condition and an increasingly frequent reason for consultation in hematology. Since the inception of the 2016 World Health Organization (WHO) 2016 criteria, in lowering the hemoglobin (Hb) and hematocrit (Hct) diagnostic thresholds to 165 g/L and 49% in men, and 160 g/L and 48% in women, respectively 1 , it has been estimated that 4.1% of unselected males (outpatients) have Hb levels exceeding these values 2 . With only a minority of these having polycythemia vera (PV) 3 , hematologists are witnessing a new preponderance of referrals for secondary erythrocytosis (SE) which has yielded novel and significant diagnostic and therapeutic challenges. While the classic coupling of JAK2 –positive/subnormal serum erythropoietin (Epo) greatly increases the likelihood of PV diagnosis 4 , 5 , those not strictly fulfilling these criteria represent a heterogeneous population for whom a systematic approach has been difficult to establish 6 . Though efforts have been made to operationally discriminate between the various forms of erythrocytosis, data comparatively assessing SE and PV populations are scarce 7 , 8 . These support different clinical profiles 7 , 8 , while reports of outcomes, including thrombosis, have been inconsistent 9 , 10 . Furthermore, little information exists on how these populations are managed in a real world setting, and it may be speculated that SE cohorts are subject either to under or over investigating and treatment. We conducted a direct comparison of clinical and laboratory features, outcomes, diagnostic workup, and treatment patterns in cohorts with SE vs WHO-defined PV.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".