348 Comparative Analysis of Post-Op Recovery in Robotic Versus Non-Robotic Resections in Inflammatory Bowel Disease Cases
Bibliographic record
Abstract
Abstract Aim Robotic surgery has increased in colorectal surgery due to its high-definition views, minimally invasive approach and enhanced dexterity. This study is to evaluate the comparative difference in post-operative recovery for Robotic Resections (RR) versus Non-Robotic Resections (NRR) in Inflammatory Bowel Disease (IBD), including Crohn’s Disease (CD), Ulcerative Colitis (UC) and Benign cases. Method A single centre retrospective comparative analysis of IBD cases that underwent RR versus NRR between 2021 to 2024. The study compared post operative lengths of admission, complications and blood tests. These included post-operative Day 1,3 and 5 White Cell Count (WCC), Haemoglobin (Hb) and C-Reactive Protein (CRP). Results A total 171 cases were analysed of which, 83 RR aged 18 to 70 years (mean 38.4, SD 12.6) and 88 NRR (Laparotomy n=62, Laparoscopic n=26) aged 17 to 80 years (mean 40.6, SD 15.1). The RR included 58 CD, 22 UC, 3 Benign cases and NRR included 76 CD, 10 UC and 2 Benign cases. The average post-operative length of admission was 8.2 days (SD 4.8) for RR and 9.5 (SD 7) for NRR. 90% (n=75) of RR and 65.9% (n=58) NRR had no complications post-operation. On comparative analysis of blood tests, there were non-significant differences for Hb and WCC, however, average Day 1, 3 and 5 CRP for RR was noted to be lower; 84.7mg/L (SD54.9), 102.7mg/L (SD69), 63.2mg/L (54.9) respectively. Conclusions This study found that the average CRP and length of admission was lower with significantly fewer complications in Robotic cases, indicating a positive outcome on post-operative recovery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".