ROBOTIC APPROACH TO ABDOMINAL WALL RECONSTRUCTION DECREASES TOTAL POST-OPERATIVE OPIOID EXPOSURE, BUT NOT DAILY OPIOID USE, COMPARED TO OPEN APPROACH: THE RETROSPECTIVE VENTRAL HERNIA REPAIR AND POST-OPERATIVE OPIOID USE (RETRO-HERO) STUDY
Bibliographic record
Abstract
Abstract Background Many surgical specialties have shown a significant reduction in post-operative opioid use with adoption of the robotic platform. This has not been shown in General Surgery or in ventral hernia repairs. We compared post-operative opioid usage after open and robotic-assisted abdominal wall reconstruction (oAWR, rAWR). Method This is a retrospective cohort analysis of all patients with ventral hernias ranging between 5 to 15 cm who underwent either oAWR or rAWR between 01/2020 to 11/2022. Patient characteristics, surgery and length of hospital stay (LOS) information, post-operative opioid usage, and patient-reported pain scores were reviewed. Results 74 patients undergoing oAWR and 27 after rAWR in the study period met inclusion criteria. There was no difference in age, sex distribution, BMI, comorbidities, size, and hernia characteristic between the two groups. Median LOS was significantly longer for open repairs (5 (4–6) days vs. 2 (2–3) days, p <0.05). Median total in-hospital opioid use measured in mg Oral Morphine Equivalent (OME) and was significantly higher for open repairs (71.25(15–159) vs. 7.5 (0–60), p <0.05). However, there was no difference in daily opioid use, multimodal pain control, or total discharge opioid prescription. Multivariable logistic regression analysis indicates lower age is a significant contributor to high opioid use (OR = 0.95 (0.9–0.99), p =0.02) but the use of the robot platform was not (OR = 1.7 (0.56–4.95), p = 0.88). Conclusion oAWR patients had higher total post-operative opioid exposure but there is no difference in daily opioid use. Future studies should explore the potential of the robotic approach at minimizing opioids by way of standardized multimodal pain management protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".