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 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 | 0.002 | 0.008 |
| 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".