Robotic‐assisted bariatric surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Bariatric laparoscopic surgery has been shown to lead to sustainable weight-loss in obese individuals. Robotic-assisted laparoscopic surgery is proposed as the next major evolution in minimally invasive surgery. This study systematically reviews the literature regarding the feasibility and safety of robotic-assisted bariatric surgery in obese patients. METHODS: A comprehensive search of electronic databases was completed for the period 2003 to 2010. Two independent reviewers assessed the studies for relevance, inclusion, and extracted data. RESULTS: After an initial screen of 297 titles, 22 studies met the inclusion criteria. A total of 1253 patients with a mean preoperative body mass index of 46.6 kg/m(2) were obtained from 13 included studies. Major complications of malabsorptive procedures included eight anastomotic leaks (2.4%), bleeding (7/349 patients = 2%) and strictures/stenosis (13/430 patients = 3%). There were no reported deaths. CONCLUSIONS: This systematic review demonstrates that robotic-assisted bariatric surgery is both a safe and feasible option for severely obese patients.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".