The Epidemiology of Myeloproliferative Neoplasms in New Zealand between 2010 and 2017: Insights from the New Zealand Cancer Registry
Bibliographic record
Abstract
Background: There is a paucity of data on ethnic disparities in patients with the classical Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs): polycythaemia vera (PV), essential thrombocythaemia (ET) and primary myelofibrosis (PMF). Methods: This study analysed the demographic data for PV, ET and PMF collected by the New Zealand Cancer Registry (NZCR) between 2010 and 2017. Results: We found that the NZCR capture rates were lower than average international incidence rates for PV and ET, but higher for PMF (0.76, 0.99 and 0.82 per 100,000, respectively). PV patients were older and had worse outcomes than expected, which suggests these patients were reported to the registry at an advanced stage of their disease. Polynesian patients with all MPN subtypes, PV, ET and PMF, were younger than their European counterparts both at the time of diagnosis and death (p < 0.001). Male gender was an independent risk factor for mortality from PV and PMF (hazard ratios (HR) of 1.43 and 1.81, respectively; p < 0.05), and Māori ethnicity was an independent risk factor for mortality from PMF (HR: 2.94; p = 0.006). Conclusions: New Zealand Polynesian patients may have increased genetic predisposition to MPN, thus we advocate for modern genetic testing in this ethnic group to identify the cause. Further work is also required to identify modifiable risk factors for mortality in MPN, in particular those associated with male gender and Māori ethnicity; the results may benefit all patients 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".