Age-Specific Trends of Invasive Cervical Cancer Incidence in British Columbia, Canada, 1971–2017
Bibliographic record
Abstract
This study examined invasive cervical cancer (ICC) incidence trends in British Columbia (BC) by age and stage-at-diagnosis relative to World Health Organization ICC elimination targets (4 per 100,000 persons). Incident ICC cases (1971-2017) were obtained from the BC Cancer Registry. Annual age-standardized incidence rates (ASIRs) per 100,000 persons were generated using the direct method. ASIRs were examined among all ages 15+ years and eight age groups using Joinpoint Regression with the Canadian 2011 standard population. Standardized rate ratios (SRRs) compared stage II-IV (late) versus stage I (early) ASIRs by age (2010-2017). ICC ASIRs did not reach the elimination target. ASIRs declined from 18.88 to 7.08 per 100,000 persons (1971-2017). Stronger declines were observed among ages 45+ years, with the largest decline among ages 70-79 years (AAPC = -3.2%, 95% CI = -3.9% to -2.6%). Among ages 25-69 years, varying levels of attenuation in declining trends and stabilization were observed since the 1980s. SRRs indicated higher rates of late-stage ICC among ages 55+ years (SRR-55-69 years = 1.34, 95% CI = 1.08-1.71). Overall, ICC incidence declined in BC since 1971 but did not reach the elimination target. The pace of decline varied across age groups and increased with age. Continued efforts are needed to progress cervical cancer elimination among all age groups.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| 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) | 0.003 | 0.001 |
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".