Classical Myelo-Proliferative Neoplasms emergence and development based on real life incidence and mathematical modeling
Bibliographic record
Abstract
Abstract Mathematical modelling allows us to better understand the emergence and evolution of myeloproliferative neoplasms. We tested different mathematical models on a first cohort (patients) (Côte d’Or Registry) to determine the onset and evolution times before JAK2V617F classical myeloproliferative disorders (polycythemia vera and essential thrombocythemia) are diagnosed. We considered the time to diagnosis as the sum of two periods: the time (from embryonic development) for the JAK2V617F mutation to appear, not disappear and enter proliferation, and a second period corresponding to the expansion of the clonal population until diagnosis. Using increasingly complex models, we show that the rate of active mutation cannot be constant, but rather increases exponentially with age, following the well-known Gompertz model. We found that it takes an average of 63.1 +/- 13 years for the first tumor cell to appear and start proliferating. On the other hand, the expansion time is constant: 8.8 years once the mutation has occurred. These results were validated in an external cohort (national FIMBANK cohort). Using this model, we analyzed JAK2V167F Essential Thrombocythemia versus Polycythemia Vera and found that the time to active mutation in PV is about 1.5 years longer than in ET, while the expansion time is similar. In conclusion, our multi-step approach and the final age-dependent model for the onset and development of MPN shows that the onset of a JAKV617F mutation should be linked to an ageing mechanism and indicates a period of 8-9 years for the development of a full MPN.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".