Evaluation of pilot experience with robotic-assisted proctectomy and coloanal anastomosis for rectal cancer
Bibliographic record
Abstract
BACKGROUND: Robotic-assisted proctectomy with coloanal anastomosis (RPCA) is an innovative technique of pelvic dissection for low rectal cancer. Our objective was to evaluate our pilot experience with this procedure compared with open proctectomy with coloanal anastomosis (OPCA). METHODS: We performed a retrospective 5-year review of all consecutive cases of RPCA and OPCA performed at our institute. We focused on tumour characteristics, quality of surgery, analgesic requirements, average length of hospital stay (LOS), complications and long-term outcomes. RESULTS: Three patients underwent RPCA and 25 had OPCA. The average duration of surgery was similar (288 min for RPCA v. 285 min for OPCA). Four patients in the OPCA group had positive or very close margins, and 2 had a mesorectal defect less than 5 mm. The average LOS was 6.66 and 9.29 days in the RPCA and OPCA groups, respectively, and the average duration of epidural or patient-controlled anesthesia was 2.67 and 5.16 days, respectively. We did not perform a statistical comparison because of the discordant size and sex distribution between the groups. There were no perioperative complications in the RPCA group, and all patients had negative margins and adequate lymph node retrievals with no long-term complications or recurrence recorded so far. CONCLUSION: Our very early experience with RPCA is quite encouraging, suggesting that it is a safe alternative to OPCA with a similar duration and the added benefits of a minimally invasive procedure, including decreased LOS and reduced postoperative analgesic requirements.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".