Obesity and Breast Cancer Prognosis: Evidence, Challenges, and Opportunities
Bibliographic record
Abstract
Purpose To summarize the evidence of an association between obesity and breast cancer prognosis. Methods We reviewed the literature regarding overweight and obesity and breast cancer survival outcomes, overall and with regard to breast cancer subtypes, breast cancer therapies, biologic mechanisms, and possible interventions. We summarize our findings and provide clinical management recommendations. Results Obesity is associated with a 35% to 40% increased risk of breast cancer recurrence and death and therefore poorer survival outcomes. This is most clearly established for estrogen receptor-positive breast cancer, with the relationship in triple-negative and human epidermal growth factor receptor 2-positive subtypes less well established. A range of biologic mechanisms that may underlie this association has been identified. Weight loss and lifestyle interventions, as well as metformin and other obesity-targeted therapies, are promising avenues that require further study. Conclusion Obesity is associated with inferior survival in breast cancer. Understanding the nature and mechanisms of this effect provides an important opportunity for interventions to improve the diagnosis, treatment, and outcomes of obese patients with breast cancer.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".