684 From Obesity to Oncology: Bariatric Surgery and the Impact on Breast - What Is the Link? – A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Aim Obesity is a complex, progressive and relapsing chronic disease associated with significant morbidity and mortality, including breast cancer. Bariatric surgery is the single most efficacious treatment modality for obesity. This review examined the prevalence of breast cancer diagnosis in those that undergo bariatric surgery, as well as the effect of menopausal status on breast cancer prevalence in those that undergo bariatric surgery. Method Our review was conducted in accordance with PRISMA guidelines; involving the search of databases including Cochrane Library, Embase, Scopus, PubMed and Google Scholar. Study quality was assessed using the Newcastle-Ottawa scale (NOS). Statistical analysis was performed using Review Manager (Revman) Version 5.4. Results A total of 22,88003 patients were included in the analysis. There was a statistically significant risk reduction in breast cancer risk in those that underwent bariatric surgery (RR 0.58 95% confidence interval (CI) 0.46, 0.72, p <0.001) across all Fourteen study groups. There was significant heterogeneity across the studies (Chi2= 358 p < 0.00001, I2 = 96%). There was no statistically significant reduction in premenopausal breast cancer risk (RR 0.88 95% CI 0.74, 1.04) or in the postmenopausal cohort (RR 0.46 CI 95% 0.18, 1.19). Conclusions Bariatric surgery results in a statistically significant reduction in breast cancer incidence. Additionally, we have found that bariatric surgery may reduce the incidence of both pre and postmenopausal breast cancer, although the four studies analysed failed to achieve statistical significance and further evaluation regarding the role of menopausal status on breast cancer incidence in those that undergo a bariatric procedure is required.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".