How common are myeloproliferative neoplasms? A systematic review and meta‐analysis
Bibliographic record
Abstract
Myeloproliferative neoplasms (MPNs) are a heterogeneous group of diseases including polycythemia vera (PV), essential thrombocythemia (ET), and primary(idiopathic) myelofibrosis (PMF). In this systematic review, we provide a comprehensive report on the incidence and prevalence of MPNs across the globe. Electronic databases (PubMed, EMBASE, MEDLINE, and Web of Science) were searched from their inception to August 2012 for articles reporting MPN incidence or prevalence rates. A random effects meta-analysis was undertaken to produce combined incidence rates for PV, ET, and PMF. Both heterogeneity and small study bias were assessed. Thirty-four studies were included. Reported annual incidence rates ranged from 0.01 to 2.61, 0.21 to 2.27, and 0.22 to 0.99 per 100,000 for PV, ET, and PMF, respectively. The combined annual incidence rates for PV, ET, and PMF were 0.84, 1.03, and 0.47 per 100,000. There was high heterogeneity across disease entities (I(2) 97.1-99.8%) and evidence of publication bias for ET and PMF (Egger test, P = 50.007 and P ≤ 0.001, respectively).The pooled incidence reflects the rarity of MPNs. The calculated pooled incidence rates do not reflect MPN incidence across the globe due to the high unexplained heterogeneity. Improved, widespread registration of MPNs would provide better information for global comparison of the incidence and prevalence of MPNs.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".