Overweight and prognosis in triple-negative breast cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
We conducted a systematic review and meta-analysis investigating the association between overweight and outcome in triple-negative breast cancer (TNBC) patients. We searched PubMed and Embase using variations of the search terms triple-negative breast cancer (population), overweight and/or obesity (exposure), and prognosis (outcome). Based on the World Health Organization guidelines for defining overweight, we included longitudinal observational studies, which utilized survival statistics with hazard ratios (HRs) in our analysis. The included studies measured body mass index at the time of diagnosis of TNBC and reported disease-free survival and/or overall survival. Study quality was assessed with the Newcastle-Ottawa Scale and study data were extracted using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist, independently by two authors. Random-effects models were used to combine the effect sizes (HRs), and the results were evaluated and adjusted for possible publication bias. Thirteen studies of 8,944 TNBC patients were included. The meta-analysis showed that overweight was associated with both shorter disease-free survival (HR = 1.26; 95%CI: 1.09-1.46) and shorter overall survival (HR = 1.29; 95%CI: 1.11c1.51) compared to normal-weight. Additionally, our Bayesian meta-analyses suggest that overweight individuals are 7.4 and 9.9 times more likely to have shorter disease-free survival and overall survival, respectively. In conclusion, the available data suggest that overweight is associated with shorter disease-free and overall survival among TNBC patients. The results should be interpreted with caution due to possible publication bias.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".