A three-pronged approach to evaluating robotic surgery
Bibliographic record
Abstract
Robotic surgery has been rapidly adopted in many specialties, yet barriers remain. The current manuscript outlines a gynecologic oncology division’s experience with robotic surgery and breaks down results from its robotic surgery program into three parts: (I) clinical outcomes, (II) patient-reported outcomes, and (III) hospital outcomes. Published articles, manuscripts in submission, and internal data from various studies within our division were collated. Clinical outcomes were collected from patients’ electronic health records, patient-reported outcome measures [e.g., satisfaction, quality of life (QOL), pain] were summarized from questionnaires, and hospital outcomes (e.g., resource utilization, workflow, costs) were gathered from internal hospital systems. The current review focuses on all surgeries performed for gynecologic cancers (uterine, cervical, and epithelial ovarian cancer) in the Division of Gynecologic Oncology at the Jewish General Hospital, McGill University, Montreal, Canada. In comparison to open surgery, robotic surgery was associated with fewer complications, less blood loss, and less postoperative analgesic use, without compromising recurrence rates or survival. Overall, patients reported being satisfied with the procedure and reported a relatively rapid return to daily activities and to baseline QOL. From the surgeon’s perspective, the robotic system’s user interface enabled performing minimally invasive surgeries in complex surgical cases that were performed by laparotomy prior to the introduction of robotics. From an institutional perspective, the robotic surgery cases were associated with cost savings and entailed operational efficiencies. A division of gynecologic oncology could reap a variety of benefits with the implementation of a robotic surgery program if used carefully and within a setting conducive to such technological change. The overarching implications of a computer-assisted robotic interface in the operating room extend beyond the conventional outcomes measured in healthcare.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".