The impact of myeloproliferative neoplasms (MPNs) on patient quality of life and productivity: results from the international MPN Landmark survey
Bibliographic record
Abstract
Myelofibrosis (MF), polycythemia vera (PV), and essential thrombocythemia (ET) are myeloproliferative neoplasms (MPNs) associated with high disease burden, reduced quality of life (QOL), and shortened survival. To assess how MPNs affect patients, we conducted a global MPN Landmark survey. This online survey of patients with MPNs and physicians was conducted in Australia, Canada, Germany, Japan, Italy, and the United Kingdom. The survey measured MPN-related symptoms and the impact of MPNs on QOL and the ability to work as well as disease-management strategies. Overall, 219 physicians and 699 patients (MF, n = 174; PV, n = 223; ET, n = 302) completed the survey; 90% of patients experienced MPN-related symptoms. The most frequent and severe symptom was fatigue. Most patients experienced a reduction in QOL, including those with low symptom burden or low-risk scores. A substantial proportion of patients reported impairment at work and in overall activity. Interestingly, physician feedback and blood counts were the most important indicators of treatment success among patients, with improvements in symptoms and QOL being less important. Regarding disease management, our study revealed a lack of alignment between physician and patient perceptions relating to communication and disease management, with patients often having different treatment goals than physicians. Overall, our study suggested that therapies that reduce symptom burden and improve QOL in patients with MPNs are crucial in minimizing disease impact on patient daily lives. Additionally, our findings showed a need for improved patient-physician communication, standardized monitoring of symptoms, and agreement on treatment goals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".