Gastrointestinal Complications of Laparoscopic/Robot-Assisted Urologic Surgery and a Review of the Literature
Bibliographic record
Abstract
Gastrointestinal injuries that occur during or after laparoscopic and robot-assisted surgery are serious side effects that affect patient outcome. In this review, we attempt to highlight the identification, incidence and management of gastrointestinal and visceral complications of laparoscopic and robot-assisted surgery. A search of Medline and PubMed databases was performed using the following terms: gastrointestinal complications of laparoscopy, laparoscopic, kidney and robotic surgery. A total of 1,072 papers related to the subject were analyzed. Forty-six of these papers were included in the present review. These papers reported high numbers of participants and had a high level of evidence. Gastrointestinal complications during laparoscopic and robot-assisted surgery are rare, but similar, and can occur at any time between access and closure. Despite their infrequency, these complications can result in mortality. The early recognition and management of gastrointestinal complications is very important. Unrecognized or delayed identification of gastrointestinal complications may cause sepsis and death.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".