Breast and Cervical Cancer Screening Behaviours among Colorectal Cancer Survivors in Nova Scotia
Bibliographic record
Abstract
PURPOSE: We analyzed patterns and factors associated with receipt of breast and cervical cancer screening in a cohort of colorectal cancer survivors. METHODS: Individuals diagnosed with colorectal cancer in Nova Scotia between January 2001 and December 2005 were eligible for inclusion. Receipt of breast and cervical cancer screening was determined using administrative data. General-population age restrictions were used in the analysis (breast: 40-69 years; cervical: 21-75 years). Kaplan-Meier and Cox proportional hazards models were used to assess time to first screen. RESULTS: Of 318 and 443 colorectal cancer survivors eligible for the breast and cervical cancer screening analysis respectively, 30.1% [95% confidence interval (ci): 21.2% to 39.0%] never received screening mammography, and 47.9% (95% ci: 37.8% to 58.0%) never received cervical cancer screening during the study period. Receipt of screening before the colorectal cancer diagnosis was strongly associated with receipt of screening after diagnosis (hazard ratio for breast cancer screening: 4.71; 95% ci: 3.42 to 6.51; hazard ratio for cervical cancer screening: 6.83; 95% ci: 4.58 to 10.16). CONCLUSIONS: Many colorectal cancer survivors within general-population screening age recommendations did not receive breast and cervical cancer screening. Future research should focus on survivors who meet age recommendations for population-based cancer screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".