Outcomes of Robotic Surgery for Low-Volume Surgeons
Bibliographic record
Abstract
When the outcomes are equivalent to the open technique, conventional laparoscopy is a preferred surgical approach because of its minimal invasiveness. However, outcomes following laparoscopy depend on the surgeon’s expertise, and there is a significant learning curve to attain efficiency in complex reconstructing laparoscopic procedures. Robotic assistance bridges the gap between open and laparoscopic procedures and allows surgeons with limited laparoscopy experience to offer the benefits of minimally invasive surgery to their patients. While existing data do not show better outcomes with robot assistance compared with laparoscopy for most procedures, these studies are based on data from high-volume surgeons and centers. In reality, a significant number of surgeries are performed by low-volume centers and surgeons, and robotic assistance may enable them to offer benefits of minimally invasive surgery equivalent to those of higher volume centers since robotic assistance is associated with a shorter learning curve than laparoscopy. We review the data on the outcomes of robotic surgery for low-volume surgeons with a focus on centers and surgeons in Asia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".