Association of obesity with breast cancer outcome in relation to cancer subtypes.
Bibliographic record
Abstract
11557 Background: Obesity at breast cancer (BC) diagnosis is associated with poor outcome, although the magnitude of effect in different BC subtypes is uncertain. Here we report on the association of obesity at BC diagnosis with disease-free (DFS) and overall survival (OS) in the following subtypes: (i) hormone receptor (ER/PgR) +ve, HER2-ve, (ii) HER2+ve, any ER/PgR and (iii) triple negative (TN). Methods: We searched MEDLINE, EMBASE and COCHRANE databases to December 31, 2018 and meeting presentations (past 5 years) using predefined search terms. Study eligibility, data abstraction were performed independently by two authors; those reporting hazard ratios (HR) for obesity and DFS/OS in BC subtypes were included. Using Review Manager pooled HRs were computed and weighted using generic inverse variance in fixed and random effects models (results were similar, random effects are presented). Results: Of 10,702 titles, 26 studies (108,793 patients) were included. Pooled HR for DFS for obese vs non-obese were (i) ER/PgR+ve HER2-ve 1.21 (95% Confidence interval, CI; 1.12-1.31, p < 0.00001), (ii) HER2+ve, any ER/PgR 1.16 (95%CI, 1.06-1.26; p = 0.0006) and (iii) TN, 1.13 (95%CI; 1.05-1.22 p = 0.002). Pooled HRs for OS were (i) ER/PgR+ve, HER2-ve 1.45 (95%CI; 1.30-1.62 p < 0.00001), (ii) HER2+ve any ER/PgR 1.21 (95%CI; 1.10-1.34 p = 0.0001) and (iii) TN 1.13 (95%CI, 1.04-1.23, p = 0.003).PooledHR for OS (but not DFS) were somewhat higher in observational vs interventional studies in (i) ER/PgR+ve, HER2-ve 1.57 vs 1.36, HER2+ve any ER/PgR (ii) 1.37 vs 1.09 but not (iii) TN 1.12 vs 1.22 (p = 0.21, 0.03 and 0.48, respectively). Conclusions: Obesity was associated with a worse outcome in all BC subtypes. Higher HR for OS in observational studies in (i) ER/PgR+ve, HER2- and (ii) HER2+ve any ER/PgR BC may reflect selection of healthier patients for intervention trials.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| 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) | 0.005 | 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".