Optimizing Nonopioid Analgesia and Different Pain Management Options following Abdominoplasty: A Systematic Literature Review
Bibliographic record
Abstract
Introduction: Postoperative pain remains a significant challenge with the growing number of abdominoplasties every year. Opioids are currently considered the mainstay modality for controlling postoperative pain. However, opioid-related side effects raise the need for a safer and more effective approach. In this study, we aimed to investigate these alternative evidence-based postoperative pain relief modalities following abdominoplasty. Methods: This systematic review was designed and conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The literature was systematically searched in December 2021 using the following databases: MEDLINE, Cochrane, and EMBASE. The MeSH terms used to aid the search were the following: abdominoplasty, postoperative pain management, postoperative analgesia, pain control, analgesia, and pain. Results: Reviewing the literature resulted in a total of 851 publications. After implementing our criteria, only 13 articles were included in this study, with 990 patients. A continuous infusion pump was the most commonly used method of analgesia (n = 3), followed by a transversus abdominis plane block (n = 2). The postoperative pain assessment scale was mentioned in nine out of the 13. Compared to controls, all interventions resulted in considerably lower pain levels in all the patients. Patient satisfaction was reported in three studies, and all studies reported higher satisfaction rates than the control groups. Conclusions: The authors performed a systematic review of the existing database of high-quality research on pain management after cosmetic abdominoplasty to determine the best pain management options currently available. However, future studies are recommended to assess the optimum dosing and administration methods.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".