Myeloproliferative neoplasms (MPNs) – Part 1: An overview of the diagnosis and treatment of the “classical” MPNs
Bibliographic record
Abstract
Myeloproliferative neoplasms (MPNs) are rare, yet potentially life-threatening, disorders caused by overproliferation of bone marrow stem cells. The symptom burden experienced by patients with the BCR-ABL1-negative MPNs (also referred to as the classical MPNs, i.e., essential thrombocythemia [ET], polycythemia vera [PV] and myelofibrosis [MF]) can be significant and can negatively impact quality of life (QOL). Since patients with these MPNs can live for several years, thereby requiring long-term treatment and follow-up, nurses play an essential role in communicating with these patients, assessing their symptoms, and educating them on treatments and self-management strategies that can reduce their symptom burden. This article, which is the first of a two-part series, was developed to provide nurses and other healthcare professionals with a review of the diagnosis and treatment of the most common classical MPNs. The second article in this series (also available in this issue) will provide nurses with practical guidance for managing the symptom burden associated with MPNs in order to help enhance the overall health and well-being of patients living with these disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".