Clinical outcomes of single-incision robotic cholecystectomy versus conventional 3-port laparoscopic cholecystectomy
Bibliographic record
Abstract
Background: Few studies have compared the surgical results of single-incision robotic cholecystectomy (SIRC) with those of conventional laparoscopic cholecystectomy (CLC). The purpose of this study was to evaluate the relative clinical efficacy of SIRC by comparing the number of postoperative days, pain level and complications between the 2 surgical methods. Methods: We retrospectively collected demographic, perioperative and postoperative data for all patients who underwent SIRC or CLC performed by a single surgeon from June 2016 to May 2017. Operative time was recorded, divided into anesthesia time, docking time, console time and total operation time. Postoperative pain was measured with the Numerical Pain Rating Scale. Results: A total of 121 patients underwent cholecystectomy during the study period, of whom 61 had SIRC and 60 had CLC. The mean total operation time of SIRC and CLC was 93.52 (SD 20.27) minutes and 37.67 (SD 19.73) minutes, respectively (p < 0.001). The total operation time excluding console time of SIRC was significantly longer than that of CLC (82.77 [SD 18.27] min v. 37.67 [SD 19.73] min) (p < 0.001). The mean Numerical Pain Rating Scale score was 4.73 (SD 1.23) (SIRC: 4.75 [SD 1.24]; CLC: 4.70 [SD 1.22]) (p = 0.8) within 1 hour after the operation; scores after 6 hours and 1 day decreased in a similar manner in the 2 groups (p = 0.1). Conclusion: Postoperative pain, use of an additional port, complication rates, operation time and cost of SIRC were similar to or greater than those of CLC. Large randomized controlled trials are needed to examine the true benefits of SIRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".