Time to follow‐up of an abnormal mammogram in women with diabetes: a population‐based study
Bibliographic record
Abstract
Women with diabetes have a higher breast cancer incidence and mortality. They are also significantly less likely to undergo screening mammography and present with more advanced stage than women without diabetes. The purpose of this study was to examine if women with diabetes are more likely to have delays in follow-up of abnormal mammograms, compared to women without diabetes. Using population-based health databases, this retrospective cohort study examined women between the ages of 50 and 74, with and without diabetes, living in the province of Ontario, Canada, who underwent screening through a centralized program and who had an abnormal mammogram between 2003 and 2012. We compared rates of follow-up of a diagnostic test within 180 days, as well as likelihood of mastectomy or excision procedure and a diagnosis of breast cancer. Following an abnormal screening mammogram, 97.5% of women with diabetes had a diagnostic procedure within 180 days compared to 97.9% of women without diabetes. After adjustment for other factors, women with diabetes were only 3% less likely to have follow-up testing after an abnormal mammogram than women without diabetes (hazard ratio [HR] 0.97, 95% CI: 0.96-0.99, P < 0.001). The majority of Ontario women who underwent screening mammography through a centralized screening program had timely follow-up of an abnormal mammogram, with no meaningful delays in those who had diabetes. The results of this study suggest that diagnostic delays after screening do not significantly contribute to higher breast cancer mortality in women with diabetes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".