The efficacy and safety of non-narcotic anesthesia in obstetrics and gynecology surgeries: a systematic review
Bibliographic record
Abstract
Introduction: Researchers have always been investigating the non-narcotic medications for obstetrics and gynecology surgeries. The present study was performed with aim to investigate the efficacy and safety of using anesthesia without narcotics compared to conventional anesthesia with narcotics in the treatment of pain and complications after surgery.Methods: In this systematic review study, according to the PRISMA statement, electronic databases of PubMed, Medline, Embase, Scopus and Cochrane library, as well as the Persian databases of SID and Magiran were searched with the help of Google Scholar between 1990 and the end of 2022. The studies which compared anesthesia with narcotic-based and non-narcotic-based drugs in a clinical trial or observational study design were extracted and qualitative evidence was synthesized from the results of the studies.Results: In this systematic review, 11 studies including anesthesia induction drugs, anesthesia maintenance and postoperative pain management were examined. For post-surgical pain management, non-opioid drugs such as ketorolac, paracetamol, paracetamol + ketorolac, tenoxicam and diclofenac were used, and in the case of insufficient pain management, opioid rescue drugs such as tramadol, morphine and fentanyl were also used. In general, non-opioid drugs such as paracetamol and non-steroid anti-inflammatory medications are safer than opioids and, in some cases, can be as effective or more effective in providing pain relief and anesthesia; however this pain relief and anesthesia varies depending on the type of drug. The quality of extracted evidence was low according to the Cochrane and Newcastle-Ottawa risk of bias scale.Conclusion: The use of anesthesia induction and maintenance techniques using non-opioid substances in cesarean and other gynecological surgeries can help to reduce the consumption of opioids and related side effects, such as cardiopulmonary arrest, improve pain control and improve recovery after surgery.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".