54P Overweight and prognosis in triple-negative breast cancer patients: A systematic review and meta-analysis
Bibliographic record
Abstract
The purpose is to conduct a systematic review and meta-analysis evaluating the impact of overweight on prognosis in triple-negative breast cancer (TNBC) patients. Systematic searches were conducted in PubMed and Embase using variations of the search terms triple-negative breast neoplasms (population), overweight and/or obesity (exposure), and prognosis (outcome). Data were extracted from longitudinal observational studies, which used survival statistics with hazard ratios (HRs) to examine disease-free survival and/or overall survival according to body mass index measured at the time of diagnosis of TNBC. Overweight was defined using the World Health Organization guidelines. Guided by the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist, study data were extracted and study quality assessed with the Newcastle-Ottawa Scale independently by two authors. The effect sizes (HRs) were combined with random-effects models, and the results were evaluated and adjusted for possible publication bias. The study selection process identified 11 eligible studies of 5,556 TNBC patients. The pooled estimates indicated that, relative to non-overweight, overweight was associated with both shorter disease-free survival (HR=1.33; 95%CI: 1.13–1.58) and shorter overall survival (HR=1.39; 95%CI: 1.15-1.69). Supplementary Bayesian meta-analyses showed strong evidence for non-zero effects, with the alternative hypothesis being 12.7 and 13.5 times more likely than the null-hypothesis for disease-free survival and overall survival, respectively. Relative to non-overweight, overweight was associated with a shorter disease-free and overall survival among TNBC patients.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".