Pre-diagnosis lifestyle, health history and psychosocial factors associated with stage at breast cancer diagnosis – Potential targets to shift stage earlier
Bibliographic record
Abstract
BACKGROUND: Early detection of breast cancer improves survival, so identifying factors associated with stage at diagnosis may help formulate cancer prevention messages tailored for higher risk women. The goal of this study was to evaluate associations between multiple potential risk factors, including novel ones, measured before a breast cancer diagnosis and stage at diagnosis in women from Alberta, Canada. METHODS: Women enrolled in Alberta's Tomorrow Project completed health and lifestyle questionnaires on average 7 years before their breast cancer diagnosis. The association of previously identified and novel predictors with stage (I, II and III + IV) at diagnosis were simultaneously evaluated in partial proportional odds ordinal (PPO) regression models. RESULTS: The 492 women in this study were predominantly diagnosed in Stage 1 (51.4%), had college or university education (75.4%), were married or had a partner (74.6%), had been pregnant (90.2%), had taken birth control pills for any reason (86.8%), and had an average body mass index of 26.6. Most had at least one mammogram (83%) with five mammograms the average number. Nearly all reported previously having a breast health examination from a medical practitioner (92.5%). Statistically significant factors identified in the PPO model included protective ones (older age at diagnosis, high household income, parity, smoking, spending time in the sun during high ultraviolet times, having a mammogram and high daily protein intake) and ones that increased risk of later stage at diagnosis (a comorbidity, current stressful situations and high daily caloric intake). CONCLUSION: Shifting breast cancer stage at diagnosis downwards may potentially be achieved through cancer prevention programs that target higher risk groups such as women with co-morbidities, non-smokers and younger women who may be eligible for breast cancer screening.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".