Health Care Utilization, Lifestyle, and Emotional Factors and Mammography Practices in the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Women with a history of chest radiotherapy have an increased risk of breast cancer; however, many do not undergo annual recommended screening mammography. We sought to characterize the relationship between mammography and potentially modifiable factors, with the goal of identifying targets for intervention to improve utilization. METHODS: Of 625 female participants sampled from the Childhood Cancer Survivor Study, who were treated with chest radiotherapy, 551 responded to a survey about breast cancer screening practices. We used multivariate Poisson regression to assess several lifestyle and emotional factors, health care practices, and perceived breast cancer risk, in relation to reporting a screening mammogram within the last two years. RESULTS: Women who had a Papanicolaou test [prevalence ratio (PR): 1.77; 95% confidence interval (CI) 1.26-2.49], and who perceived their breast cancer risk as higher than the average woman were more likely to have had a mammogram (PR, 1.26; 95% CI, 1.09-1.46). We detected an attenuated effect of echocardiogram screening [PR, 0.70; 95% CI (0.52-0.95)] on having a mammogram among older women compared with younger women. Smoking, obesity, physical activity, coping, and symptoms of depression and somatization were not associated with mammographic screening. CONCLUSION: Our findings suggest that compliance with routine and risk-based screening can be an important indicator of mammography in childhood cancer survivors. In addition, there is a need to ensure women understand their increased breast cancer risk, as a means to encouraging them to follow breast surveillance guidelines. IMPACT: Screening encounters could be used to promote mammography compliance in this population.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".