Comparing cervical cancer stage at diagnosis in immigrant women and long-term residents.
Bibliographic record
Abstract
e18054 Background: Globally, cervical cancer is the fourth most common cancer in women and seventh most common cancer overall. Cervical cancer is highly preventable with HPV vaccination and screening. Previous work has shown that immigrants are less likely to be screened than non-immigrants. Building on this work, the objective of our study was to examine whether immigrant women are more likely to present with later stage cervical cancer than long-term residents. Methods: We conducted a retrospective cohort study of women with cervical cancer diagnosed from 2010 to 2014 using administrative health data from the Canadian province of Ontario, comparing the odds of late stage diagnosis between immigrants and long-term residents. The outcome of interest was stage of cervical cancer diagnosis, defined as early (stage I) or late (stage II-IV). We compared immigrants and long-term residents on late vs. early stage adjusting for socioeconomic measures, comorbidities and healthcare utilization. We also confirmed results with a cohort from 2007-2012. Results: Complete staging data was available for 218 immigrants and 874 non-immigrants. We found no association between immigrant status and stage at diagnosis (adjusted OR: 0.935, p value = 0.739). Factors that did show significant association with later stage diagnosis were physician characteristics, whether a woman had been previously screened, or having visited a gynecologist in the past 3 years. These results were echoed in the 2007-2012 cohort (immigrants vs. long-term residents OR: 0.942, adjusted p value = 0.6773). Conclusions: Our results show that being an immigrant is not associated with late stage diagnosis of cervical cancer, although getting screened or having visited a gynecologist did reduce the odds of later stage cancer. Our findings support previous studies showing that physician characteristics influence immigrant healthcare utilization. Moreso, it may be that programs broadly aimed immigrants 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".