Impact of Obesity on Sentinel Lymph Node Mapping in Patients with Endometrial Intraepithelial Neoplasia Undergoing Robotic Surgery: A Retrospective Cohort Study
Bibliographic record
Abstract
Background/Objectives: Lymph node (LN) assessment for cases of endometrial intraepithelial neoplasia (EIN), a known precursor to endometrial cancer (EC), is a topic of debate. Some experts believe this practice could avoid re-staging of disease and influence the decision to administer adjuvant treatment. However, it is known that obtaining sentinel lymph node (SLN) biopsies in patients with an elevated body mass index (BMI) can be more challenging. We thus sought to evaluate the effect of BMI on the SLN detection rate (DR) during robotic hysterectomy in EIN cases. Methods: We conducted a retrospective chart review for patients with a pre-operative diagnosis of EIN who underwent robotic hysterectomy with SLN sampling. Five BMI categories were determined according to the literature. Distribution normality was assessed with the Kolmogorov–Smirnov test. Continuous variables, non-parametric continuous variables and categorical variables were assessed with the appropriate statistical tests (two-tailed Student’s t-tests, Mann–Whitney U-tests, and chi-squared tests, respectively). Results: 115 patients were included (average BMI of 34.75 ± 9.38 SD). The bilateral SLN DR was not significantly different between BMI groups (p = 0.606). The difference in unilateral SLN DR between BMI groups was also non-significant (p = 0.269). When examining high BMI subgroups (BMI > 30 and BMI > 40), no significant difference was found in bilateral nor unilateral SLN DR. A logistic regression model showed that for every unit of BMI, the likelihood of SLN DR did not change significantly. Conclusions: We found no connection between obesity (BMI > 30) or morbid obesity (BMI > 40) and reduced SLN DR in EIN cases, nor a significant variation in the DR when comparing all the different BMI subgroups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".