Comparison of Intraoperative Outcomes between Single-incision Robotic Cholecystectomy and Multi-incision Robotic Cholecystectomy
Bibliographic record
Abstract
Background The aim of this study was to evaluate the differences in the key surgical factors for single-incision robotic cholecystectomy (SIRC) and multi-incision robotic cholecystectomy (MIRC). Methods A retrospective data review from August 2013 to April 2018 consisting of 104 SIRC and 105 MIRC cases was done considering factors including patient gender, age, operating time (skin incision to skin closure), robotic console time (docking to undocking), the preoperative diagnosis for surgery, any complications in surgery, length of stay (LOS), and estimated blood loss (EBL). Procedures with conversion away from original robotic cholecystectomy approach were excluded. Chi-square analysis (p-value: 0.05) was run between the two data sets. Results A total of 209 robotic cholecystectomy cases were reviewed since 2013. We found significantly less time with single-incision compared to multi-incision (single incision = 94.0 minutes, multi-incision = 99.9 minutes, p = 0.016) and EBL (single-incision = 11.52 mL, multi-incision = 17.17 mL, p = 0.004). There was no significant difference in age or robotic console time. The most common indication was symptomatic cholelithiasis overall, with equal cases of dyskinesia in single-incision approach, although there was no significant difference in indication between the two approaches. Intraoperatively, there was marginally significant use of irrigation in multi-incision (multi-incision 45 [42.9%], single-incision 31 [29.8%], p = 0.0499) and no difference in Firefly, perforation, or intraoperative cholangiogram use. LOS results showed significant decreased stay in SIRC cases (single-incision 84 outpatients [80.8%], multi-incision 75 [71.4%]; p = 0.0379). Conclusions SIRC and MIRC are both safe and feasible ways to remove the inflamed/dysfunctional gallbladder. SIRC is associated with less operative time, less blood loss, and shorter hospital stay.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".