Cancer survival among <scp>F</scp>irst <scp>N</scp>ations people of <scp>O</scp>ntario, <scp>C</scp>anada (1968–2007)
Bibliographic record
Abstract
We aimed to compare cancer survival in Ontario First Nations people to that in other Ontarians for five major cancer types: colorectal, lung, cervix, breast and prostate. A list of registered or "Status" Indians in Ontario was used to create a cohort of over 140,000 Ontario First Nations people. Cancers diagnosed in cohort members between 1968 and 2001 were identified from the Ontario Cancer Registry, with follow-up for death until December 31st, 2007. Flexible parametric modeling of the hazard function was used to compare the survival experience of the cohort to that of other Ontarians. We considered changes in survival from the first half of the time period (1968-1991) to the second half (1992-2001). For other Ontarians, survival had improved over time for every cancer site. For the First Nations cohort, survival improved only for breast and prostate cancers; it either declined or remained unchanged for the other cancers. For cancers diagnosed in 1992 or later, all-cause and cause-specific survival was significantly poorer for First Nations people diagnosed with breast, prostate, cervical, colorectal (male and female) and male lung cancers as compared to their non-First Nations peers. For female lung cancer, First Nations women appeared to have poorer survival; however, the result was not statistically significant. Ontario's First Nations population experiences poorer cancer survival when compared to other Ontarians and strategies to reduce these inequalities must be developed and implemented.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".