The Myeloproliferative Neoplasm Landscape: A Patient’s Eye View
Bibliographic record
Abstract
Patients with myeloproliferative neoplasms (MPNs), a group of rare haematological conditions including polycythaemia vera, essential thrombocythaemia, and myelofibrosis, often experience a range of symptoms which can significantly impact their quality of life (QoL). Although symptom burden is highest in myelofibrosis and high-risk patients, lower-risk patients also report symptoms impacting their daily life and ability to work. In addition to physical symptoms, MPNs affect emotional well-being, with anxiety and depression frequently reported by patients. Despite significant advances in treatment options, such as the introduction of JAK1/JAK2 inhibitors, therapy for MPNs is often palliative; therefore, reduction of symptoms and improvement of QoL should be considered as major treatment goals. One of the main issues impacting MPN treatment is the discord between patient and physician perceptions of symptom burden, treatment goals, and expectations. New technologies, such as app-based reporting, can aid this communication, but are still not widely implemented. Additionally, regional variation further affects the psychosocial burden of MPNs on patients and their associates, as treatments and access to clinical trials are options for patients living in some areas, but not others. Overcoming some of the challenges in patient–physician communication and treatment access are key to improving disease management and QoL, as well as giving the patient greater input in treatment decisions. Myeloproliferative neoplasms (MPNs) are a group of blood diseases where the body makes too many blood cells. Patients with MPNs can have symptoms which interfere with their daily lives, such as tiredness, pain, sweating at night, dizziness, itching, and difficulty sleeping. They also often suffer from anxiety and/or depression. In nearly all cases, physicians cannot cure the disease, but drugs can prevent blood clots and reduce the speed at which the disease gets worse. Usually, the main aim of treatment is improving patients’ quality of life (QoL). Targeted drugs, such as ruxolitinib, treat MPNs and reduce symptoms, but do not cure the disease. Patients frequently want to play a bigger part in decisions about their treatment. However, physicians and patients often have different views on how well treatments are working and what to expect from the treatment. This can mean that patients feel they are not getting the best treatment for their symptoms. Also, patients may not be able to get some treatments or take part in a trial of a new drug, depending on where they live. This creates feelings of unfairness which can affect their mental health. Addressing all these problems may help improve the QoL for patients with these blood diseases.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".