Myeloproliferative neoplasm patient symptom burden and quality of life: Evidence of significant impairment compared to controls
Bibliographic record
Abstract
The myeloproliferative neoplasms (MPN) including polycythaemia vera (PV), essential thrombocythaemia and primary myelofibrosis (PMF) are rare diseases contributing to significant morbidity. Symptom management is a prime treatment objective but current symptom assessment tools have not been validated compared to the general population. The MPN-symptom assessment form (MPN-SAF), a reliable and validated clinical tool to assess MPN symptom burden, was administered to MPN patients (n = 106) and, for the first time, population controls (n = 124) as part of a UK case-control study. Mean symptom scores were compared between patients and controls adjusting for potential confounders. Mean patient scores were compared to data collected by the Mayo Clinic, USA on 1,446 international MPN patients to determine patient group representativeness. MPN patients had significantly higher mean scores than controls for 25 of the 26 symptoms measured (P < 0.05); fatigue was the most common symptom (92.4% and 78.1%, respectively). Female MPN patients suffered worse symptom burden than male patients (P < 0.001) and substantially worse burden than female controls (P < 0.001). Compared to the Mayo clinic patients, MPN-UK patients reported similar symptom burden but lower satiety (P = 0.046). Patients with PMF reported the worst symptom burden (88.3%); significantly higher than PV patients (P < 0.001). For the first time we report quality of life was worse in MPN-UK patients compared with controls (P < 0.001).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".