Perioperative Pain Management in Bariatric Anesthesia
Bibliographic record
Abstract
Weight loss (bariatric) surgery is the most commonly performed elective surgical procedure in patients with morbid obesity. In this review, we provide an evidence-based update on perioperative pain management in bariatric anesthesia. We mention some newer preoperative aspects-medical optimization, physical preparation, patient education, and psychosocial factors-that can all improve pain management. In the intraoperative period, with bariatric surgery being almost universally performed laparoscopically, we emphasize the use of non-opioid adjuvant infusions (ketamine, lidocaine, and dexmedetomidine) and suggest some novel regional anesthesia techniques to reduce pain, opioid requirements, and side effects. We discuss some postoperative strategies that additionally focus on patient safety and identify patients at risk of persistent pain and opioid use after bariatric surgery. This review suggests that the use of a structured, step-wise, severity-based, opioid-sparing multimodal analgesic protocol within an enhanced recovery after surgery (ERAS) framework can improve postoperative pain management. Overall, by incorporating all these aspects throughout the perioperative journey ensures improved patient safety and outcomes from pain management in bariatric anesthesia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".