Analgesic interaction between ondansetron and acetaminophen after tonsillectomy in children: The<scp>P</scp>aratron randomized, controlled trial
Bibliographic record
Abstract
BACKGROUND: The mechanism of action of acetaminophen remains unclear. One hypothesis involves an interaction with the serotoninergic system. Antagonists to serotonin (5-HT)3 receptors (setrons) have antiemetic properties. Therefore, co-administration of acetaminophen and a setron could lead to a decrease or a loss of acetaminophen analgesic effects. The aim of this study was to demonstrate such an interaction. METHODS: Paratron is a prospective, randomized, controlled, double-blind, parallel group trial. All children aged 2-7 years (n = 69) scheduled for a tonsillectomy ± adenoidectomy received intraoperative acetaminophen with ondansetron or droperidol. Pain scores [Children's Hospital of Eastern Ontario Pain Scale (CHEOPS)], morphine consumption and the incidence of post-operative nausea and vomiting (PONV) were measured for 24 h following surgery. RESULTS: Pain scores were not different at all times between the groups but median morphine consumption (μg) in recovery was 322.5 [interquartile range (IQR) 0.0-500.0] and 0 (IQR 0-0) in the ondansetron (n = 35) and droperidol (n = 34) groups, respectively (p = 0.004). The percentages of patients who received morphine titration were 57.1% and 20.6% in the ondansetron and droperidol groups, respectively (p = 0.008). No significant difference was found for PONV. CONCLUSIONS: An interaction between acetaminophen and ondansetron is suggested, with children receiving three times more morphine during pain titration in the recovery room. More studies are necessary to evaluate whether this finding is clinically relevant enough to preclude the simultaneous perioperative administration of both drugs in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".