Robotic-assisted Laparoscopic Colorectal Surgery
Bibliographic record
Abstract
Robotic assistance provides a number of potential benefits for laparoscopic surgery by addressing several inherent limitations. However, its utility in colorectal surgery has not been determined. This is a report of our initial experience with robot-assisted colon resections. We prospectively followed 10 patients who underwent robotic-assisted laparoscopic colorectal surgery using Zeus Microwrist System. Surgical outcomes were compared with those of 10 consecutive patients who underwent laparoscopic colorectal surgery in the same institution for similar indications prior to the start of robotic-assisted surgery. Six patients in each group had surgery for colorectal malignancy. All 10 robotic-assisted procedures were completed with no intraoperative complications, conversions, or mortality. The average blood loss was less than 150 mL in all cases. Morbidity and hospital stay were comparable to those for the patients undergoing standard laparoscopic procedures. Robotic surgery was associated with a significant increase in operative time of almost 1 hour. This time was reduced significantly after the first 4 cases. The value of robotic assistance in colorectal surgery needs to be further evaluated in a larger comparative study.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".