Security and Feasibility of Laparoscopic Rectal Cancer Resection in Morbidly Obese Patients
Bibliographic record
Abstract
Background: Rectal resection for cancer can be technically challenging, especially in the obese patient. While some have investigatedthe impact of laparoscopic surgery on rectal cancer, no study looked at the subgroup of morbidly obese patients.Objectives: Our goal was to evaluate feasibility and safety of laparoscopic rectal resection for cancer in this population.Methods: All morbidly obese patients, defined as a body mass index (BMI) of 40 kg/m2 or greater, undergoing laparoscopic rectalcancer resection for primary cancer between January 2006 and July 2013, were identified using medical records in a single academichospital center.Results: Thirteen patients underwent laparoscopic approach. The median BMI was 42.4 kg/m2. There were 4 conversions (30%).Anastomotic leak occurred in 2 patients (15.4%). TME was complete in only 9 patients (69.2%), with 3 patients with incomplete TMEbeing also in the conversion group. There was no mortality. There was no recurrence.Conclusions: This study suggests that laparoscopic rectal resection for cancer in morbidly obese patients is challenging and associatedwith a higher rate of conversion compared to patients with lower BMI. Mortality, morbidity and readmission rates are similarto the literature showing the same benefit for laparoscopic procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".