Laparoscopic bariatric surgery with hysterectomy for endometrial cancer to improve long-term outcomes: A review article
Bibliographic record
Abstract
Endometrial cancer is the most common gynecologic malignant disease in Canada, and women with an elevated body mass index carry an increased lifetime risk of developing this disease. As rates of obesity have risen, the rates of endometrial cancer have seen a similar rise. Given this association, women diagnosed with endometrial cancer often suffer from several obesity-related co-morbidities, including type 2 diabetes, hypertension, and cardiovascular disease. Studies have suggested that women with early-stage endometrial cancer have a higher potential to die of obesity-related complications than recurrence and that weight reduction would be beneficial for these patients to improve quality-of-life and long-term obesity-related morbidity and mortality. Bariatric surgery is currently the only treatment modality to result in sustained long-term weight loss for this cohort of patients. Early evidence has suggested that combining bariatric surgery with total laparoscopic hysterectomy may improve the underlying metabolic disease, the patients' quality of life, and the long-term obesity-related morbidity and mortality. Additionally, the inclusion of bariatric surgery in treatment of patients with obesity and endometrial cancer may decrease the projected lifetime health care cost when compared to hysterectomy alone. This narrative review aims to examine the evidence surrounding endometrial cancer and its link to obesity. Further, we hope to explore current treatments for the aforementioned conditions and the possible benefits and feasibility of a combined intervention of vertical sleeve gastrectomy and total laparoscopic hysterectomy for patients with obesity and endometrial cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".