Impact of mutations on pregnancy outcome in patients with myeloproliferative neoplasms
Bibliographic record
Abstract
The impact of driver and other somatic mutations on pregnancy outcomes is unknown. The purpose of this study was to report the management and outcome of pregnancies in a cohort of myeloproliferative neoplasms (MPN) patients, particularly to evaluate the impact of somatic mutations. The cohort included consecutive patients with MPN who had a least one confirmed pregnancy. The primary outcome was live births. Secondary outcomes were thrombotic and major bleeding events. Between 2010 and 2021, 29 pregnancies occurred in 24 individuals with MPN. Aspirin was used in 24 cases (83%) and interferon alfa in five (17%). There were 24 live births (83%). There were three thrombotic events, two antepartum and one postpartum. Miscarriages and thrombotic events occurred in JAK2-mutated and triple negative, but not CALR-mutated, MPN. Additional somatic mutations were rare, and there were no apparent associations with pregnancy loss or complications. While JAK2 V617F is associated with an increased risk of thrombosis, its impact on pregnancy outcome has been inconsistently reported. The association between triple negative MPN and adverse pregnancy outcome has not previously been reported. While limited by small numbers, this study underscores the importance of describing driver and other mutations to direct optimal antenatal care in individuals with 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".