Obesity and endocrine therapy: Host factors and breast cancer outcome
Bibliographic record
Abstract
Obesity is becoming increasingly prevalent and it has been linked to poor breast cancer outcomes. Because obesity is associated with increased adipose tissue mass and aromatase activity [the target of aromatase inhibitors (AIs)], there is concern that these agents may be less effective in women who are overweight or obese. Four of the randomized trials of AIs vs. tamoxifen conducted in the adjuvant breast cancer setting (ATAC, BIG 1-98 and TEAM in the postmenopausal setting and ABCSG-12 in the premenopausal setting) have reported effects of body mass index (BMI) on the relative effectiveness of an AI vs. tamoxifen. Obesity was confirmed as an adverse prognostic factor in ATAC and BIG 1-98 but not the TEAM study; in ABSCG-12, obesity was associated with poor outcomes in the anastrozole arm only. In the three postmenopausal trials, the use of an AI vs. tamoxifen was associated with better outcomes at all levels of BMI [all hazard ratios for recurrence <1, although 95% confidence intervals often included 1 due to lower power and smaller reductions in risk]. Of note, there was no significant interaction of BMI with letrozole (vs. tamoxifen) in the BIG 1-98 trial; while ATAC investigators concluded that the relative benefit of anastrozole (vs. tamoxifen) might be better in thinner (vs. heavier) women. In ABCSG-12, the use of anastrozole (vs. tamoxifen) was associated with significantly worse outcomes in women with BMI ≥25 kg/m(2) (similar to the detrimental effect of anastrozole on overall survival seen in the parent trial). These findings do not support the use of BMI as a predictor of AI (vs. tamoxifen) benefit in the adjuvant setting in postmenopausal breast cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".