Patterns and Predictors of Screening for Breast and Cervical Cancer in Women with CKD
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Breast and cervical cancers are prevalent in women with CKD, but it is uncertain how often screening for these cancers should be undertaken given concerns that the benefits of screening may be fewer and the harms greater in women with CKD than in the general population. We examined patterns of breast and cervical cancer screening in women on the basis of CKD stage and age and assessed predictors of screening. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted two population-based cohort studies (breast and cervical cancer screening) from 2002 to 2013 using linked administrative health care data from Ontario, Canada. A total of 141,326 and 324,548 women were included in the breast and cervical cancer screening cohorts, respectively. RESULTS: The 2-year cumulative incidences were 61% among women without CKD, 54% for those with CKD stages 3a and 3b, 37% for those with CKD stages 4 and 5, and 26% for women on dialysis. Similar patterns were observed for the 3-year cumulative incidences of cervical cancer screening. The associations of breast and cervical cancer screening with CKD were modified by age and CKD stage, where lower incidence of screening in women with advanced CKD compared with no CKD was most pronounced in older age groups (P<0.001). Older age, higher comorbidity burden, and lower-income groups were associated with a lower rate of screening. CONCLUSIONS: Most women with advanced CKD do not receive breast or cervical cancer screening. A better understanding of patient and health professional preferences toward cancer screening in CKD is needed along with the outcomes of such 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 | 0.001 | 0.003 |
| 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".