An Approach to the Investigation of Thrombocytosis: Differentiating between Essential Thrombocythemia and Secondary Thrombocytosis
Bibliographic record
Abstract
Background . Thrombocytosis is a common reason for referral to Hematology. Differentiating between secondary causes of thrombocytosis and essential thrombocythemia (ET) is often clinically challenging. A practical diagnostic approach to identify secondary thrombocytosis could reduce overinvestigation such as next generation sequencing (NGS) panel. Methods and Results . All adult patients with thrombocytosis (≥450 × 10 9 /L) who underwent molecular testing at a single tertiary care centre between January 1, 2018 and May 31, 2021 were evaluated. Clinical and laboratory variables were compared between patients with secondary thrombocytosis vs. ET. Clinical variables included smoking, thrombosis, splenectomy, active malignancy, chronic inflammatory disease, and iron deficiency anemia. Laboratory variables included complete blood count (CBC), ferritin, and myeloid mutations detected by NGS. The overall yield of molecular testing was 52.4%; 92.1% of which were mutations in JAK2 , CALR , and/or MPL . Clinical factors predictive of ET included history of arterial thrombosis ( p < 0.05); active malignancy, chronic inflammatory disease, splenectomy, and iron deficiency were associated with secondary thrombocytosis ( p < 0.05). A diagnosis of ET was associated with higher hemoglobin, mean corpuscular volume (MCV), red cell distribution width (RDW), and mean platelet volume (MPV), while secondary thrombocytosis was associated with higher body mass index, white blood cells, and neutrophils ( p < 0.01). Conclusion . A practical approach to investigating patients with persistent thrombocytosis based on clinical characteristics such as active malignancy, chronic inflammatory disease, splenectomy, and iron deficiency may assist in accurately identifying patients more likely to have secondary causes of thrombocytosis and reduce overinvestigation, particularly costly molecular testing.
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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) | 0.001 | 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".