Obesity and endocrine therapy: Host factors and breast cancer outcome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it