Survival of Older Women With Cervical Cancer Based on Screening History
Bibliographic record
Abstract
OBJECTIVE: A population-level retrospective cohort study was conducted to determine the influence of cervical screening history on the survival from cervical cancer in women 50 years or older. METHODS: The study included women diagnosed with invasive cervical cancer in Ontario, Canada, between 2005 and 2012, who were followed for at least 4 years. Screening history was observed for the 5 years before diagnosis. Health care administrative databases were linked to determine demographic, affiliation with primary care physicians, stage (available 2010-2012), treatment, and survival data. Kaplan-Meier and multivariate analyses were carried out to evaluate the impact of cervical screening on overall survival (OS). RESULTS: There were eligible 1,422 women diagnosed with invasive cervical cancer between 2005 and 2012 of whom 566 had been screened within the 5 years before diagnosis. There were 856 women who did not undergo screening within the 5 years before diagnosis. Unscreened women were more likely to present with locally advanced disease (69.3%) compared with the screened women (42.9%). Four-year OS was significantly greater in the screened group (79.9% vs 58.2%). In our univariate analysis, screening was significantly related to survival (hazard ratio = 2.1, p < .01). In our multivariate analysis after adjusting for age, treatment, affiliation with a primary care physician, and income, screening was still significantly associated with improved survival (hazard ratio = 1.5, p < .01). CONCLUSIONS: Our results demonstrate a survival benefit to screening in women 50 years or older who are diagnosed with cervical cancer. Screening participation must be encouraged in women older than 50 years as rates decline in this age group.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".