Rare cancers in Canada, 2006–2016: A population-based surveillance report and comparison of different methods for classifying rare cancers
Bibliographic record
Abstract
BACKGROUND: The cumulative burden from rare cancers has not been adequately explored in Canada. This analysis aims to characterize the occurrence of rare cancers among Canadians and estimate the probability of being diagnosed with a rare cancer among cancer patients with different demographic characteristics. METHODS: The Canadian Cancer Registry was used for this analysis. Cancer types were classified in three ways: using the SEER site recode scheme; by histology group; and by site/histology group. The age-standardized incidence rate (ASIR) and 95 % confidence intervals (CI) for each cancer type was estimated for diagnoses from 2006 to 2016. Two ASIR thresholds were used to classify cancers as rare:6/100,000/year and 15/100,000/year. Log-binomial regression was used to estimate the adjusted probability of having a rare cancer among those with cancer by age, sex and geographic region. RESULTS: Using the 6/100,000/year threshold, the incidence proportion (IP) of rare cancers ranged from 9.7 %(95 %CI:9.6,9.7 %)-17.0 %(95 %CI:16.9,17.0 %), and ranged from 19.2 %(95 %CI:19.1,19.3 %)-52.5 %(95 %CI:52.0,53.0 %) using the <15/100,000/year threshold. The adjusted probability of being diagnosed with a rare cancer was highest among those aged ≤19 years. There was higher concordance in estimates of the burden of rare cancers across methods to classify cancer types when the lower incidence rate threshold was used to define rare cancers. INTERPRETATION: This analysis yielded evidence that rare cancers comprise a substantial proportion of annual cancer diagnoses among Canadians. Findings from this analysis point to using a lower incidence rate threshold, to generate estimates of the burden of rare cancers that are robust to different cancer classification schemes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".