Combined Surgery for Uterine and Renal Cell Cancers in Obese Patients: A Case Series
Bibliographic record
Abstract
We describe our experience with concomitant surgery for synchronously diagnosed uterine and renal cell cancer, two obesity-linked malignancies, to better identify the challenges posed by such patients. Our institution’s tumor registry and renal cell cancer database were queried for patients with both coincident cancers who were treated from 2000 to present day. The medical records of these patients were systematically reviewed. Six patients were synchronously diagnosed with both uterine and renal cell cancer and underwent combined surgical management. Five of these were performed by an open approach and one by using a minimally invasive surgery (MIS) technique. The majority of patients who had open surgery experienced significant operative or perioperative morbidity. One of these five patients died on postoperative day 3 due to complications from surgery in the setting of significant medical comorbidities. Two other patients with open surgery required splenectomy due to iatrogenic injury at the time of nephrectomy. An MIS technique was utilized for the patient with the largest body mass index (61 kg/m 2 ). This patient recovered without complications. Our experience with combined surgery for coincident uterine and renal cell cancer suggests caution when planning such a procedure. An open approach carries with it significant risk for morbidity, especially in comorbid patients. An MIS approach should be considered when feasible. Synchronous diagnoses of these cancers are rare, but may become more common with the increasing prevalence of obesity. J Clin Gynecol Obstet. 2016;5(3):92-96 doi: http://dx.doi.org/10.14740/jcgo412w
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".