Reduced Screening Mammography Among Women With Diabetes
Bibliographic record
Abstract
BACKGROUND: Despite regular health care, preventive health issues may be neglected in patients with chronic diseases such as diabetes. Case-control studies in the United States have shown lower mammogram rates in women with diabetes; however, it is not known whether the presence of diabetes mellitus affects mammography use in a Canadian setting, where there is universal access to health care. METHODS: Using health databases in Ontario from April 1, 1999, to March 31, 2002, this retrospective cohort study observed women aged 50 to 67 years, who were free of breast cancer, until their first mammogram in a 2-year period. Mammogram rates were compared between women who had had diabetes for a minimum of 2 years (n = 69 168) and women without diabetes (n = 663 519). RESULTS: Compared with women without diabetes, diabetic patients were older, had more physician visits, were more often from a lower-income neighborhood, and, in those 65 years or older, were less likely to be taking estrogen. The odds ratio of having a mammogram during the 2-year period was 0.68 (95% confidence interval, 0.67-0.70; P<.001) for women with diabetes, and adjustment for age and other covariates did not modify this effect. CONCLUSIONS: Women with diabetes were significantly less likely to have had a mammogram during a 2-year period than were women without diabetes, despite more health care visits. These results suggest that, because of the complexity involved in diabetes care, routine preventive care such as cancer screening is often neglected. These findings highlight the need for better organization of primary care for patients with chronic diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".