Comparing stage of diagnosis of cervical cancer at presentation in immigrant women and long-term residents of Ontario: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Globally, cervical cancer is the fourth most common cancer in women and 7th most common cancer overall. Cervical cancer is highly preventable with screening. Previous work has shown that immigrants are less likely to undergo screening than nonimmigrants in Ontario, Canada. We examined whether immigrant women are more likely to present with later stage cervical cancer than long-term residents of the province. METHODS: We conducted a retrospective matched cohort study of women with cervical cancer diagnosed between 2010 and 2014 using provincial administrative health data. We compared the odds of late-stage diagnosis between immigrants and long-term residents, adjusting for socioeconomic measures, comorbidities and health care use. The outcome of interest was stage of cervical cancer diagnosis, defined as early (stage I) or late (stages II-IV). We confirmed results with a cohort of women with cancer diagnosed between 2007 and 2012. RESULTS: Complete staging data were available for 218 immigrants and 1348 matched long-term residents. We found no association between immigrant status and stage at diagnosis (adjusted odds ratio [OR] 0.94, 95% confidence interval [CI] 0.63-1.39). Factors that did show significant association with late-stage diagnosis were physician characteristics, whether a woman had previously undergone screening and had visited a gynecologist in the past 3 years. These results were echoed in the 2007-2012 cohort (immigrants v. long-term residents, OR 0.94, 95% CI 0.71-1.20). INTERPRETATION: Our results show that being an immigrant is not associated with late-stage diagnosis of cervical cancer in Ontario. Programs broadly aimed at immigrants may require a targeted approach to address higher-risk subgroups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".