Body Mass Index Before and After Breast Cancer Diagnosis: Associations with All-Cause, Breast Cancer, and Cardiovascular Disease Mortality
Bibliographic record
Abstract
BACKGROUND: Factors related to improving outcomes in breast cancer survivors are of increasing public health significance. We examined postdiagnosis weight change in relation to mortality risk in a cohort of breast cancer survivors. METHODS: We analyzed data from a cohort of 3,993 women with ages 20 to 79 years living in New Hampshire, Massachusetts, or Wisconsin with invasive nonmetastatic breast cancers diagnosed in 1988 to 1999 identified through state registries. Participants completed a structured telephone interview 1 to 2 years after diagnosis and returned a mailed follow-up questionnaire in 1998 to 2001 that addressed postdiagnosis weight and other factors. Vital status information was obtained from the National Death Index through December 2005. Hazard ratios and 95% confidence intervals were estimated from Cox proportional hazards models and adjusted for prediagnosis weight, age, stage, smoking, physical activity, and other important covariates. RESULTS: During an average 6.3 years of follow-up from the postdiagnosis questionnaire, we identified 421 total deaths, including 121 deaths from breast cancer and 95 deaths from cardiovascular disease. Increasing postdiagnosis weight gain and weight loss were each associated with greater all-cause mortality. Among women who gained weight after breast cancer diagnosis, each 5-kg gain was associated with a 12% increase in all-cause mortality (P = 0.004), a 13% increase in breast cancer-specific mortality (P = 0.01), and a 19% increase in cardiovascular disease mortality (P = 0.04). Associations with breast cancer mortality were not modified by prediagnosis menopausal status, cigarette smoking, or body mass index. CONCLUSION: These findings suggest that efforts to minimize weight gain after a breast cancer diagnosis may improve survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".