Relationship between body mass index (BMI) at diagnosis of ER+ node negative breast cancer (BC) and Oncotype DX recurrence score.
Bibliographic record
Abstract
582 Background: Inferior stage-adjusted survival for BC among obese (O) women has been reported. This may reflect differences in treatment planned for O women, in treatment received (due to toxicity), or in tumor biology arising in different cellular environments, such as high serum insulin levels, which are associated with a higher risk of recurrence. Methods: We examined whether overweight (BMI 25-30) or obese (BMI >30) women had a higher Oncotype Dx Recurrence Score (RS) after ER+, node negative (NN) or N0i+ BC than normal weight women. Included were all participants at the BC Cancer Agency in a RS clinical utility study in consecutive ER+ NN, N0i+ BC (n=156) at the BC Cancer Agency and in the TAILORx trial (n=56). Sixteen were excluded (n=5 withdrew consent; 1 triple negative; 3 test failed; 2 her2+; 1 neoadjuvant treatment, n=4 height and weight missing). Results: A similar proportion of O patients had low, intermediate, and high RS tumors. Cancers with a high RS were more likely to be grade 3 (55% of grade 3 tumours had high RS, 33% intermediate RS, 12% low RS) and fewer were strongly ER positive (69% of high RS versus 97% of intermediate and low RS). Conclusions: While this data does not support differences in tumor risk arising in O versus non obese environments in ER+, NN BC, examination of a larger data set may be more informative. [Table: see text]
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".