Colorectal cancer incidence in the Aboriginal population of Ontario, 1998 to 2009.
Bibliographic record
Abstract
BACKGROUND: Studies suggest that colorectal cancer incidence increased disproportionately among the Aboriginal population of Ontario relative to the general population. Using an ecological approach, this study examined colorectal cancer incidence for the 1998-to-2009 period among Aboriginal people living in Ontario. DATA AND METHODS: Based on their postal code when they were diagnosed, cases of colorectal cancer identified from the Ontario Cancer Registry were assigned to census geographic areas with high (33% or more) or low percentages of Aboriginal identity residents, using the Postal Code Conversion File Plus (PCCF+). To account for potential misclassification by the PCCF+, Indian reserves for which assignment through postal codes is likely to be accurate were identified. Age-standardized incidence rates and rate ratios were calculated to compare colorectal cancer incidence in high-Aboriginal identity areas or on Indian reserves with incidence in low-Aboriginal identity areas. RESULTS: Colorectal cancer incidence was significantly higher for residents of high- versus low-Aboriginal identity areas in Ontario (rate ratio for men = 1.44, 95% CI = 1.26-1.63; rate ratio for women = 1.42, 95% CI = 1.23-1.63), a disparity that persisted by age group. When the Aboriginal sample was limited to residents of Indian reserves, the difference was statistically significant only for men and for people aged 50 to 74. INTERPRETATION: The incidence of colorectal cancer differs across areas of Ontario with high and low percentages of Aboriginal identity residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".