A prospective comparison of costs between robotics, laparoscopy, and laparotomy in endometrial cancer among women with Class III obesity or higher
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To compare the immediate operating room (OR), inpatient, and overall costs between three surgical modalities among women with endometrial cancer (EC) and Class III obesity or higher. METHODS: A multicentre prospective observational study examined outcomes of women, with early stage EC, treated surgically. Resource use was collected for OR costs including OR time, equipment, and inpatient costs. Median OR, inpatient, and overall costs across surgical modalities were analyzed using an Independent-Samples Kruskal-Wallis Test among patients with BMI ≥ 40. RESULTS: Out of 520 women, 103 had a BMI ≥ 40. Among women with BMI ≥ 40: median OR costs were $4197.02 for laparotomy, $5524.63 for non-robotic assisted laparoscopy, and $7225.16 for robotic-assisted laparoscopy (p < 0.001) and median inpatient costs were $5584.28 for laparotomy, $3042.07 for non-robotic assisted laparoscopy, and $1794.51 for robotic-assisted laparoscopy (p < 0.001). There were no statistically significant differences in the median overall costs: $10 291.50 for laparotomy, $8412.63 for non-robotic assisted laparoscopy, and $9002.48 for robotic-assisted laparoscopy (p = 0.185). CONCLUSION: There was no difference in overall costs between the three surgical modalities in patient with BMI ≥ 40. Given the similar costs, any form of minimally invasive surgery should be promoted in this population.
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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 | 0.001 |
| 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 | 0.000 |
| 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".