Cervical cancer incidence in British Columbia: Predicting effects of changes from Pap to human papillomavirus screening and of changes in screening participation
Bibliographic record
Abstract
Objectives To estimate the impact of increased participation in screening, and of the proposed change from Pap to human papillomavirus screening on the incidence of cervical cancer in British Columbia. Methods For invasive cervical cancer cases diagnosed in British Columbia between 2002 and 2011, data were extracted on age and cancer morphology from the British Columbia Cancer Registry, and Pap screening history was obtained from the British Columbia Cervical Cancer Screening Program database. Only screening performed two to seven years prior to diagnosis was assumed to reduce subsequent risk of cancer. Results from randomized trials of human papillomavirus versus cytology screening and population based estimates of cytology screening were used to estimate the effect of a change in screening test and increases in participation. Results Between 2002 and 2011, there were 1663 cases of cervical cancer reported; 660 (367 squamous and 293 non-squamous) were eligible and screened two to seven years prior to diagnosis. The predicted reduction by changing to human papillomavirus screening was 363 (95% confidence interval = 124-496) representing 22% of all cases. If 50% of subjects not screened two to seven years prior had undergone Pap screening, it is projected that a further 268 cases (16%) could have been prevented; if they had undergone human papillomavirus screening, a further 365 cases (22%) could have been prevented. Conclusions For many women who develop cervical cancer, primary human papillomavirus testing could have substantially reduced their cancer risk. Human papillomavirus rather than Pap testing would further increase the gains from any increases in population screening participation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".