Robotic‐assisted colon and rectal surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Colorectal surgery is one of the most common procedures performed by general surgeons, with an increasing number being performed laparoscopically. Robotic technology is emerging in the ongoing evolution in minimally invasive surgery. This study systematically reviews the literature regarding the safety and feasibility of robotic-assisted colorectal surgery. METHODS: A comprehensive search of electronic databases was completed for the period 2000 to 2010. Two independent reviewers assessed the studies for relevance and inclusion, and extracted data. RESULTS: After an initial screen of 347 titles, 20 studies met the inclusion criteria. A total of 854 patients were included with a mean age of 61 years and a body mass index of 25.5 kg/m(2) . Major complications included 27 anastamotic leaks (27/766 = 3.5%), 10 post-operative bleeds (1.1%) and 14 post-operative infections (1.6%). There were no mortalities reported. CONCLUSIONS: This systematic review demonstrates that robotic-assisted colorectal surgery is emerging as a safe and feasible option in colorectal surgery.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".