Postoperative pain management in non-traumatic emergency general surgery: WSES-GAIS-SIAARTI-AAST guidelines
Bibliographic record
Abstract
BACKGROUND: Non-traumatic emergency general surgery involves a heterogeneous population that may present with several underlying diseases. Timeous emergency surgical treatment should be supplemented with high-quality perioperative care, ideally performed by multidisciplinary teams trained to identify and handle complex postoperative courses. Uncontrolled or poorly controlled acute postoperative pain may result in significant complications. While pain management after elective surgery has been standardized in perioperative pathways, the traditional perioperative treatment of patients undergoing emergency surgery is often a haphazard practice. The present recommended pain management guidelines are for pain management after non-traumatic emergency surgical intervention. It is meant to provide clinicians a list of indications to prescribe the optimal analgesics even in the absence of a multidisciplinary pain team. MATERIAL AND METHODS: An international expert panel discussed the different issues in subsequent rounds. Four international recognized scientific societies: World Society of Emergency Surgery (WSES), Global Alliance for Infection in Surgery (GAIS), Italian Society of Anesthesia, Analgesia Intensive Care (SIAARTI), and American Association for the Surgery of Trauma (AAST), endorsed the project and approved the final manuscript. CONCLUSION: Dealing with acute postoperative pain in the emergency abdominal surgery setting is complex, requires special attention, and should be multidisciplinary. Several tools are available, and their combination is mandatory whenever is possible. Analgesic approach to the various situations and conditions should be patient based and tailored according to procedure, pathology, age, response, and available expertise. A better understanding of the patho-mechanisms of postoperative pain for short- and long-term outcomes is necessary to improve prophylactic and treatment strategies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".