Cancer patterns in Inuit Nunangat: 1998–2007
Bibliographic record
Abstract
OBJECTIVES: To compare cancer incidence patterns between residents of Inuit Nunangat and the rest of Canada. STUDY DESIGN: Cancer cases were geographically linked to either Inuit Nunangat or the rest of Canada using postal codes or other geographic information. Population estimates were derived from the 2001 and 2006 censuses. METHODS: Cancer cases were combined from 1998 to 2007 for Inuit Nunangat and the rest of Canada. Age-standardised incidence rates were calculated for all site cancers and sub-sites by sex. Standardised rate ratios between these 2 areas were calculated for all site cancers and sub-sites. RESULTS: The age-standardised incidence rate for all cancer sites (1998-2007) was 14% lower for the Inuit Nunangat male population and 29% higher for the female population by comparison to the rest of Canada. Cancers of the nasopharynx, lung and bronchus, colorectal, stomach (males), and kidney and renal pelvis (females), were elevated in the Inuit Nunangat population compared to the rest of Canada, whereas prostate and female breast cancers were lower in the Inuit Nunangat population. CONCLUSIONS: Cancers with potentially modifiable risk factors, such as buccal cavity and pharynx, nasopharynx, lung and bronchus, and colorectal cancer were elevated in the Inuit Nunangat population compared to the rest of Canada. Besides greater smoking prevalence within Inuit Nunangat by comparison to the rest of Canada, distinct socioeconomic characteristics between respective area populations including housing, and income may have contributed to incidence differentials. This study demonstrated that a geographic approach can be used in cancer surveillance when populations of interest are spatially distinguishable, and reside across distinct jurisdictions whose combined cancer registries will not completely provide information to identify the population of interest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".