Multistate transitional models for measuring adherence to breast cancer screening: A population-based longitudinal cohort study with over two million women
Bibliographic record
Abstract
Objective Prior work on the disparities among women in breast cancer screening adherence has been methodologically limited. This longitudinal study determines and examines the factors associated with becoming adherent. Methods In a cohort of Canadian women aged 50-74, a three-state transitional model was used to examine adherence to screening for breast cancer. The proportion of time spent being non-adherent with screening was calculated for each woman during her observation window. Using age as the time scale, a relative rate multivariable regression was implemented under the three-state transitional model, to examine the association between covariates (all time-varying) and the rate of becoming adherent. Results The cohort consisted of 2,537,960 women with a median follow-up of 8.46 years. Nearly 31% of women were continually up-to-date with breast screening. Once a woman was non-adherent, the rate of becoming adherent was higher among longer term residents (relative rate = 1.289, 95% confidence interval 1.275-1.302), those from wealthier neighbourhoods, and those who had an identifiable primary care provider who was female or had graduated in Canada. Conclusion Individual and physician-level characteristics play an important role in a woman's adherence to screening. This work improves the quality of evidence regarding disparities among women in adherence to breast cancer screening and provides a novel methodological foundation to investigate adherence for other types of screening, including cervix and colorectal 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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".