Why Do Women Get Cervical Cancer in an Organized Screening Program in Canada?
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to determine factors associated with the development of cervical malignancy among women participating in an organized cervical screening program. MATERIALS AND METHODS: A population-based retrospective cohort study was performed examining the screening histories 2 to 10 years before diagnosis of invasive cancer in Ontario women between 2011 and 2014. RESULTS: A total of 2,002 cases of cervical cancer were identified; 1,358 (68%) were squamous cell carcinomas and 644 (32%) were adenocarcinomas. The mean age at the time of diagnosis was 50.3 years. More than 60% of the cohort had at least 1 cytology test within 2 to 10 years of their diagnosis. Of the women having a cytology result 24 to 36 months before diagnosis, 69% had a normal cytology whereas only 7% had a high-grade cytology result. Stage of cancer was most advanced in women who did not have cytology in the 2 to 10 years before their diagnosis. On multivariate regression, those with cervical cancer who were less likely to have undergone screening include older age, advanced stage, lower income, not having a family physician, and those diagnosed with adenocarcinoma. CONCLUSIONS: Although nonparticipation in screening is the greatest factor associated with cervical cancer diagnosis, failure of cervical cytology to detect cytologic abnormalities in women 2 to 3 and 3 to 5 years before diagnosis is of concern. Efforts must be directed to recruitment of women for screening as well as improving the sensitivity of screening tests to detect existing abnormalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".