Postdiagnosis Weight Change and Survival Following a Diagnosis of Early-Stage Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Achieving a healthy weight is recommended for all breast cancer survivors. Previous research on postdiagnosis weight change and mortality had conflicting results. METHODS: We examined whether change in body weight in the 18 months following diagnosis is associated with overall and breast cancer-specific mortality in a cohort of n = 12,590 stage I-III breast cancer patients at Kaiser Permanente using multivariable-adjusted Cox regression models. Follow-up was from the date of the postdiagnosis weight at 18 months until death or June 2015 [median follow-up (range): 3 (0-9) years]. We divided follow-up into earlier (18-54 months) and later (>54 months) postdiagnosis periods. RESULTS: Mean (SD) age-at-diagnosis was 59 (11) years. A total of 980 women died, 503 from breast cancer. Most women maintained weight within 5% of diagnosis body weight; weight loss and gain were equally common at 19% each. Compared with weight maintenance, large losses (≥10%) were associated with worse survival, with HRs and 95% confidence intervals (CI) for all-cause death of 2.63 (2.12-3.26) earlier and 1.60 (1.14-2.25) later in follow-up. Modest losses (>5%-<10%) were associated with worse survival earlier [1.39 (1.11-1.74)] but not later in follow-up [0.77 (0.54-1.11)]. Weight gain was not related to survival. Results were similar for breast cancer-specific death. CONCLUSION: Large postdiagnosis weight loss is associated with worse survival in both earlier and later postdiagnosis periods, independent of treatment and prognostic factors. IMPACT: Weight loss and gain are equally common after breast cancer, and weight loss is a consistent marker of mortality risk. Cancer Epidemiol Biomarkers Prev; 26(1); 44-50. ©2016 AACR SEE ALL THE ARTICLES IN THIS CEBP FOCUS SECTION, "THE OBESITY PARADOX IN CANCER EVIDENCE AND NEW DIRECTIONS".
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".