Initial experiences of robotic SP cholecystectomy: a comparative analysis with robotic Si single-site cholecystectomy
Bibliographic record
Abstract
The da Vinci SP robotic surgical system (Intuitive Surgical) offers pure SP with 4 lumens, which accommodates the fully-wristed endoscope and 3 arms with multijoint feature.We herein present our initial experience of the da Vinci SP surgical system in robotic single-site cholecystectomy.Methods: Thirty consecutive patients with a preoperative diagnosis of gallstones and/or chronic cholecystitis who underwent robotic SP cholecystectomy (RSPC) using da Vinci SP surgical system from January to May 2019 were reviewed.The perioperative outcomes were assessed and compared with those performed using Si-robotic single-site surgical system.Results: Mean docking time was 5.2 minutes.The mean actual dissection time was 14.6 minutes while the mean operation time was 75.1 minutes.Postoperative course was unremarkable and patients were discharged after a mean hospital stay of 1.5 days.In comparative analysis, operation time (109.5 ± 30.0 minutes vs. 75.1 ± 17.5 minutes, P = 0.001), docking time (11.9 ± 4.3 minutes vs. 5.2 ± 1.9 minutes, P = 0.001), actual dissection time (34.6 ± 18.4 minutes vs. 14.6 ± 5.1 minutes, P = 0.001), console time (58.7 ± 23.0 minutes vs. 32.4 ± 11.6 minutes, P = 0.001), immediate postoperative pain (4.6 ± 1.3 vs. 3.2 ± 1.0, P = 0.001), and pain prior to discharge (2.0 ± 0.6 vs. 1.4 ± 0.0, P = 0.002) were significantly improved in RSPC.Conclusion: RSPC is feasible, safe, and effective.The perioperative outcomes are better compared with Si-robotic singlesite surgical systems.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".