Global patterns of <scp>non‐Hodgkin</scp> lymphoma in 2020
Bibliographic record
Abstract
We evaluated the global patterns of non-Hodgkin lymphoma (NHL) in 2020 using the estimates of NHL incidence and mortality in 185 countries that are part of the GLOBOCAN 2020 database, developed by the International Agency for Research on Cancer (IARC). As well as new cases and deaths of NHL, corresponding age-standardized (world) rates (ASR) of incidence and mortality per 100 000 person-years were derived by country and world region. In 2020, an estimated 544 000 new cases of NHL were diagnosed worldwide, and approximately 260 000 people died from the disease. Eastern Asia accounted for a quarter (24.9%) of all cases, followed by Northern America (15.1%) and South-Central Asia (9.7%). Incidence rates were higher in men than in women, with similar geographical patterns. While the incidence rates were highest in Australia and New Zealand, Northern America, Northern Europe and Western Europe (>10/100 000 for both sexes combined), the highest mortality rates (>3/100 000) were found in regions in Africa, Western Asia and Oceania. The large variations and the disproportionately higher mortality in low- and middle-income countries can be related to the underlying prevalence and distribution of risk factors, and to the level of access to diagnostic and treatment facilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".