Outcomes and Cost Comparisons After Introducing a Robotics Program for Endometrial Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of introducing a robotic program on cost and patient outcome. METHODS: This was a prospective evaluation of clinical outcome and cost after introducing a robotics program for the treatment of endometrial cancer and a retrospective comparison to the entire historical cohort. RESULTS: Consecutive patients with endometrial cancer who underwent robotic surgery (n=143) were compared with all consecutive patients who underwent surgery (n=160) before robotics. The rate of minimally invasive surgery increased from 17% performed by laparoscopy to 98% performed by robotics in 2 years. The patient characteristics were comparable in both eras, except for a higher body mass index in the robotics era (median 29.8 compared with 27.6; P<.005). Patients undergoing robotics had longer operating times (233 compared with 206 minutes), but fewer adverse events (13% compared with 42%; P<.001), lower estimated median blood loss (50 compared with 200 mL; P<.001), and shorter median hospital stay (1 compared with 5 days; P<.001). The overall hospital costs were significantly lower for robotics compared with the historical group (Can$7,644 compared with Can$10,368 [Canadian dollars]; P<.001) even when acquisition and maintenance cost were included (Can$8,370 compared with Can$10,368; P=.001). Within 2 years after surgery, the short-term recurrence rate appeared lower in the robotics group compared with the historic cohort (11 recurrences compared with 19 recurrences; P<.001). CONCLUSION: Introduction of robotics for endometrial cancer surgery increased the proportion of patients benefitting from minimally invasive surgery, improved short-term outcomes, and resulted in lower hospital costs. LEVEL OF EVIDENCE: II.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".