Preoperative weight loss in women with obesity in gynaecologic oncology: A retrospective study
Bibliographic record
Abstract
Summary To retrospectively review the efficacy of short term supervised medical weight loss for women with obesity, body mass index (BMI ≥40 kg/m2) in gynaecologic oncology, and the associated perioperative and pathologic outcomes. A retrospective study of a dedicated preoperative weight loss clinic for gynaecologic oncology patients from March to December 2019. Statistical analysis was performed with McNemar's test for correlated proportions, Pearson's correlation tests for continuous variables, and paired t‐tests to compare means. Generalized estimating equations (GEE) were used to determine the factors associated with weight loss over time. A P‐value of <.05 was used for statistical significance. Review of cases up‐graded after surgery was performed by a gynaecologic pathologist. There were a total of 49 women included in the study. The most common referral reason was endometrioid carcinoma or hyperplasia of the endometrium (77.6%). Mean initial weight was 130.2 kg, and corresponding mean BMI 48.1 kg/m2. Patients attended on average nine preoperative weight loss visits. A significant difference between initial weight and weight at surgery was demonstrated, from 129.6 to 118.0 kg (8.4% weight loss) (P < .0001). This difference persisted to their post‐surgical visit, with an additional mean loss of 1.89 kg (9.4% weight loss) (P = .044). The majority (92.1%) of patients with endometrial pathology had surgical management, and of these 85.7% were minimally invasive. Preoperative weight loss is a feasible option in gynaecologic oncology patients. Greater understanding of clinical significance, follow‐up, and ideal target population for this intervention is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".