Oral pregabalin is effective as preemptive analgesia in abdominal hysterectomy—A randomized controlled trial
Bibliographic record
Abstract
Postoperative pain is one of the main negative symptoms resulting from surgery and the use of new methods to control this symptom is of ever-increasing relevance. Opioid-sparing strategies, such as multimodal analgesia, are trends in this scenario. Pregabalin is a well-established treatment for neuropathic pain; however, it is still controversial in the surgical context for postoperative analgesia. This study investigated the effect of pregabalin on postoperative analgesia in patients undergoing abdominal hysterectomy. It is a prospective, randomised, double-blind, placebo-controlled clinical trial. Female patients undergoing abdominal hysterectomy were randomised to use pregabalin (group P1), 300 mg orally 2 h before surgery, or identical placebo pills (group P0). The main outcome includes the postoperative pain index by visual analogue scale (VAS) and McGill's pain questionnaire. Secondary outcomes include opioid consumption and the presence of adverse effects. A value of p < 0.05 was used to reject type I error. Fifty-five patients were randomised amongst the groups. Patients in group P1 had lower pain rates by VAS scale, both at rest and in active motion, than group P0. In McGill's questionnaire, patients from group P1 also had lower pain rates (12 × 28.5). There was approximately twice as much opioid consumption amongst patients in group P0. Regarding side effects, there was a difference between the two groups only for dizziness, being more incident in group P1. This study suggests that pregabalin is an important adjuvant drug in treating postoperative pain in patients with abdominal hysterectomy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".