The Burden of Rare Cancers in North America.
Bibliographic record
Abstract
Background: Rare cancers are difficult to study owing to their infrequent diagnosis. Using aggregate incidence data from population-based cancer registries in Europe, the Surveillance of Rare Cancers in Europe project compiled a list of clinically relevant, topography and morphology defined rare cancers operationally defined as having a crude annual incidence rate of <6 per 100,000 persons. In 2020, this list of rare cancers was updated. The objective of this study was to assess the utility of a rare cancer recode variable for use in the Cancer in North America (CiNA) dataset and to provide a first look at the burden of rare cancers in Canada and the United States. Methods: Data were obtained from 62 registries in Canada and the United States that met North American Association of Central Cancer Registries (NAACCR) high-quality data standards. The list of rare cancers was programmed as a Rare Cancer Classification variable within SEER*Stat. SEER*Stat was used to estimate case counts and crude and age-specific incidence rates per 100,000 for cancers diagnosed 2015-2019 by age at diagnosis, country, and country-specific geographic regions in Canada and the United States, and by race/ethnicity in the United States. Results: In Canada and the United States, 21% and 22% of all invasive cancers were classified as rare, respectively. The percentage of rare cancers ranged between 18% to 21% across geographic regions in Canada and the United States. Children (aged 0-14 years) had the highest percentage and lowest incidence rates of rare cancers. The percentage of rare cancers decreased, and incidence increased with increasing age. In the United States, Hispanics had the highest percentage (27%) and non-Hispanic Whites and non-Hispanic Blacks the lowest percentage (21%) of rare cancers. Conclusions: While individual rare cancers are infrequently diagnosed, in aggregate, they account for a substantial percentage of all cancers diagnosed in the population and pose a substantial public health burden. We report variations in percentage of rare cancers by age, and race/ethnicity (United States only). Such variations in the burden of these cancers may suggest possible areas for public health research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".