Anesthesia and analgesia for gynecological surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: High-quality analgesia has been linked to improved patient satisfaction as well as improved short-term and long-term postoperative outcomes. Acute surgical pain is a modifiable risk factor for development of chronic postoperative pain, which is reported by up to 26% of gynecologic surgical patients. In other surgical populations, multimodal analgesia has shown improved pain control and decreased reliance on opioids. This review examines recent evidence for various analgesic modalities applied specifically to the gynecologic surgical population. RECENT FINDINGS: Nonopioid agents like acetaminophen, nonsteroidal anti-inflammatories, and gamma-aminobutyric acid analogs resulted in reduction in postoperative pain and opioid consumption. Application of regional anesthetic techniques had a favorable effect that persisted beyond the immediate recovery period. Preemptive analgesia remains unproven. The best evidence for effective combinations comes from ERAS studies that incorporated multimodal analgesia into a systemic approach geared towards early discharge. SUMMARY: Multimodal analgesia had demonstrated advantages for all types of gynecological surgeries in terms of improving postoperative pain control and minimizing opioid-related adverse effects. Multimodal analgesia includes acetaminophen, NSAIDS, and gamma-aminobutyric acid analogs combined with intraoperative nonopioid analgesics such as ketamine, regional anesthesia or intrathecal morphine. Further research should focus on determining most effective combinations and doses of multimodal analgesia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".