Clinical and financial implications of robotically-assisted surgery
Bibliographic record
Abstract
The use of minimally invasive surgery (MIS) in gynecologic oncology has been limited despite an exponential growth in some other specialties. Technological advances in the field of robotics may facilitate the application of MIS, thereby allowing more patients to benefit from the less invasive procedure. Despite the rising popularity of robotic surgery, questions remain as to its clinical and cost-effectiveness, contributing to resistance to changes in clinical practice and thus impeding its growth.The objective of the current thesis was to evaluate the clinical and financial outcomes, from the perspective of patients and of the hospital, following the introduction of a robotic surgery program in gynecologic oncology.Where applicable, data was retrieved from electronic health records, hospital information systems, and a series of retrospective and prospectively managed databases in the Division of Gynecologic Oncology at a tertiary center in Canada. Patient-level data included baseline characteristics, diagnostic information, operative outcomes, clinical outcomes, self-reported questionnaires, and resource use. All studies were approved by the institution's internal review board.The use of robotics in gynecologic oncology was found to result in a relatively rapid return to preoperative quality of life and patient-rated pain. Compared to open surgery, patients who underwent robotic surgery for the treatment of endometrial cancer used significantly less analgesics, including less opioids and a diminished use of patient-controlled analgesia, and this was associated with a decrease in direct costs for the hospital. In ovarian cancer, where the use of robotics is rare, the approach was found to be feasible, improved perioperative results while maintaining oncologic outcomes, and was, on average, less expensive than open surgery. From the perspective of the hospital, the use of robotics largely replaced the use of open surgery, was found to decrease resource utilization and increase turnover on the inpatient ward, and was associated with a return on investment in the current setting.The use of robotics in gynecologic oncology continues to expand. Insofar as its use in the setting examined, the current thesis demonstrates the clinical benefits of the procedure, the ability to achieve operational efficiencies and cost savings, and the potential to be a valuable investment in a high-volume center. The conceivable areas of innovation envisioned with such a technological platform are explored.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".