Multistate transitional models for measuring adherence to breast cancer screening: A population-based longitudinal cohort study with over two million women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it