Sociodemographic characteristics of women with invasive cervical cancer in British Columbia, 2004–2013: a descriptive study
Bibliographic record
Abstract
Background: Although cancer screening has led to reductions in the incidence of invasive cervical cancer (ICC) across Canada, benefits of prevention efforts are not equitably distributed. This study investigated the sociodemographic characteristics of women with ICC in British Columbia compared with the general female population in the province. Methods: In this descriptive study, data of individuals 18 years and older diagnosed with ICC between 2004 and 2013 were obtained from the BC Cancer Registry. Self-reported sociodemographic characteristics were derived from standardized health assessment forms (HAFs) completed upon admission in the BC Cancer Registry. Standardized ratios (SRs) were derived by dividing observed and age-adjusted expected counts by ethnicity or race, language, and marital, smoking and urban–rural status. Differences between observed and expected counts were tested using χ2 goodness-of-fit tests. General population data were derived from the 2006 Census, 2011 National Household Survey and 2011/12 Canadian Community Health Survey. Results: Of 1705 total cases of ICC, 1315 were referred to BC Cancer (77.1%). Of those who were referred, 1215 (92.4%) completed HAFs. Among Indigenous women, more cases were observed (n = 85) than expected (n = 39; SR 2.16, 95% confidence interval [CI] 2.15–2.18). Among visible minorities, observed cases (n = 320) were higher than expected (n = 253; 95% CI 1.26–1.26). Elevated SRs were observed among women who self-identified as Korean (SR 1.78, 95% CI 1.76–1.80), Japanese (SR 1.77, 95% CI 1.74–1.79) and Filipino (SR 1.60, 95% CI 1.58–1.62); lower SRs were observed among South Asian women (SR 0.63, 95% CI 0.62–0.63). Elevated SRs were observed among current smokers (SR 1.34, 95% CI 1.33–1.34) and women living in rural-hub (SR 1.29, 95% CI 1.28–1.31) and rural or remote (SR 2.62, 95% CI 2.61–2.64) areas; the SR was lower among married women (SR 0.90, 95% CI 0.90–0.90). Interpretation: Women who self-identified as visible minorities, Indigenous, current smokers, nonmarried and from rural areas were overrepresented among women with ICC. Efforts are needed to address inequities to ensure all women benefit from cervical cancer prevention.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".