Short-term clinical outcomes of open, laparoscopic, and robotic-assisted rectal resections: a multicenter real-world evidence study from Indian collaborative group on rectal resections (ICGRR)
Bibliographic record
Abstract
This multi-centric real-world study was carried out to assess the perioperative and histopathological clinical outcomes of rectal resections employing open, laparoscopic, and robotic-assisted techniques. A retrospective chart review was undertaken for patients who underwent rectal resections for Stages I, II, and III rectal cancer (RC) between April 2012 and August 2023. All surgical procedures were performed with the principles of total mesorectal excision (TME) or partial mesorectal excision (for tumors located higher in the rectum). The study analyzed data from 829 patients of which 314 were in the robotic-assisted group (RAS), 206 in the laparoscopic surgery group (LG), and 309 in the open-surgery group (OG). The TNM staging and location of RC were evenly distributed across the three groups. The RAS group had a significantly lower length of hospital stay than LG and OG. Compared to LG and OG, the RAS group had less blood loss and postoperative complications, but significantly longer mean operating room time. The conversion rate of the RAS group was significantly lower than that of the LG group (p = 0.03). In comparison to the OG and LG groups, the RAS group had significantly lower (p < 0.05) rates of positive circumferential resection margin (CRM). Adjuvant treatment was administered in the RAS group significantly earlier (median, 24.5 days, IQR 18-37) compared to the LG (median, 31 days, IQR 23-41) and OG (median, 32.5 days, IQR 27-42). This largest multi‑centric study by the ICRR group has validated the value of a relatively newer technology like RAS in real-world Indian settings for rectal resections.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".