Abstract P5-08-19: The risk of breast, ovarian and endometrial cancer in obese women submitted to bariatric surgery: A meta-analysis
Bibliographic record
Abstract
Abstract Background: Obesity is related to major risk factors for a number of noncommunicable diseases including cancer. There is a high incidence of breast and endometrial cancer in obese women. Thus, we performed a systematic review followed by meta-analysis to evaluate the relationship between bariatric surgery, a method widely used to treat obesity, and the risk of developing breast, ovarian and endometrial cancer in obese women. Methods: MEDLINE, EMBASE, LILACS and Cochrane databases were searched from inception until January 2019 which retrieve studies to assessed the risk of breast, ovarian and endometrial cancer in obese women submitted to bariatric surgery. There was no language restriction. We extracted and combined data from studies in order to assess the risk ratio (RR) of developing these cancers. A random-effects, meta-analytic model was applied in all calculations. The New Castle Ottawa and GRADE were used to assess quality and bias of the included studies. Trial registered in PROSPERO (CRD42019112927). Results: We found 188 articles and only seven of those were included in our meta-analysis, which incorporated a total of 150,528 patients in the bariatric surgery arm and 1,461,938 women in the control arm. The total risk of developing breast, ovarian and endometrial cancer was 0.37 (95%CI [0.28 to 0.48]; I2=87%; 7 studies). The risk of breast cancer was reduced by 61% [RR: 0.39 (95%CI [0.24 to 0.64]; I2= 90%; 6 studies). The risk of ovarian cancer was reduced by 53% [RR: 0.47 (95%CI [0.27 to 0.81]; I2= 0%; 3 studies). The risk of endometrial cancer was reduced by 67% [RR: 0.33 (95%CI [0.21 to 0.51]; I2= 88%; 7 studies). Conclusion: Bariatric surgery may have a protective effect by reducing the risk of developing breast, ovarian and endometrial cancer in obese women. However, high heterogeneity was found and not explained by our subgroup analysis. Although we couldn’t separate the types of surgeries performed, we hypothesized that the high heterogeneity might be due to the types of surgery. Therefore, we suggest more research with appropriate report of the type of the surgery by group. Citation Format: Beatriz Pércia Ishihara, Daniela Farah, Marcelo Cunio Machado Fonseca, Afonso Celso Pinto Nazário. The risk of breast, ovarian and endometrial cancer in obese women submitted to bariatric surgery: A meta-analysis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-08-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.009 |
| 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) | 0.006 | 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 teacher head, 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".