The Impact of Prophylactic Dexamethasone on Nausea and Vomiting After Laparoscopic Cholecystectomy
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of prophylactic corticosteroid administration on postoperative nausea, vomiting, pain and complications in patients undergoing laparoscopic cholecystectomy. DATA SOURCES: We searched 4 bibliographic databases, conference proceedings, reference lists of articles and textbooks, and contacted experts in the field of anesthesia and hepatobiliary surgery. REVIEW METHODS: We evaluated the methodologic quality of trials and extracted data regarding baseline characteristics, interventions, and outcomes. We pooled results from the studies using a random-effects model, evaluated the degree of heterogeneity, and explored potential explanations for heterogeneity. RESULTS: Seventeen trials met eligibility criteria and provided high quality evidence regarding steroid effectiveness. Irrespective of the co-interventions (other antiemetic medications), dexamethasone reduced the incidence of nausea (RR 0.59, 95% CI, 0.48-0.72), vomiting (RR 0.41, 95% CI, 0.30-0.55), and postoperative nausea or vomiting (RR 0.55, 95% CI, 0.44-0.67) relative to placebo. Dexamethasone also seemed to reduce the severity of postoperative pain (Ratio of Means 0.87, 95% CI, 0.78-0.98), although substantial unexplained heterogeneity was present (I 90.4%). The incidence of headache and dizziness was similar between groups. CONCLUSIONS: Prophylactic dexamethasone decreases the incidence of nausea and vomiting after LC relative to placebo and may decrease the severity of postoperative pain. Dexamethasone does not increase the incidence of headaches or dizziness. Surgeons should consider administering prophylactic corticosteroids to patients undergoing laparoscopic cholecystectomy, particularly those at high risk of postoperative nausea and vomiting.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".