Adding haloperidol to morphine for patient-controlled analgesia (PCA) reduces nausea and vomiting after short stay surgery: randomized, controlled
Bibliographic record
Abstract
"Background: Morphine Patient-Controlled Analgesia (PCA) increases the frequency of postoperative nausea and vomiting (PONV) and the effectiveness adding haloperidol is unknown. Methods: 145 women scheduled to undergo short-stay surgery under general anaesthesia were randomly assigned in two groups: One group received 2 mg i.v. of haloperidol 30 minutes before the end of surgery plus 2 mg mixed with 50 mg of morphine for administration via PCA (Group H); the other group received the same analgesic scheme for pain management using two comparable i.v. boluses of saline (Group P). Furthermore, both groups received dexamethasone 4 mg during anaesthesia induction. Ondansetron (4 mg i.v.) was used for antiemetic rescue. Participants and outcomes assessors were blinded to group assignment. The primary endpoints were incidence of nausea, vomiting and antiemetic requirements during the first 24 hours after surgery. Secondary endpoints included sedation and morphine requirement. Results: Cumulative data at 24 hours showed that the group H had less nausea (71.2% vs. 20.6%; RR 0.29 [95% CI: 0.17-0.46]) and vomiting (47% vs. 11.8%; RR 0.25; [95% CI: 0.12-0.49]), and required less ondansetron (66.7% vs. 17.7%), but had an increased incidence of sedation (NNH: 3.5; 95% CI, 2.3-6.7). The NNT for Total response (no nausea, no vomiting/retching) was 2.5 (0-2 hours) and 2 (2-24 hours). Conclusion: A bolus of haloperidol 2 mg prior to the end of surgery followed by 2 mg mixed with 50 mg of Morphine for PCA administration can significantly reduce the frequency of PONV but at a cost of increased sedation."
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".