Economic evaluation of ondansetron vs dimenhydrinate for prevention of postoperative vomiting in children undergoing strabismus surgery
Bibliographic record
Abstract
BACKGROUND: Although rarely life-threatening, postoperative vomiting (POV) is a distressing complication. The incidence of POV ranges from 34 to 90% in children undergoing strabismus surgery when antiemetics are not administered prophylactically. METHODS: In this study, a cost-consequence analysis (CCA) is used to estimate the economic benefit of ondansetron and dimenhydrinate as antiemetics administered prophylactically in children undergoing strabismus surgery. This retrospective study was conducted at The Hospital for Sick Children based on a review of 70 charts. RESULTS: Ondansetron was more effective with 45.3 POV-free patients (PFP) in an adjusted cohort of 100, while dimenhydrinate resulted in 38.2 PFP in an adjusted cohort of 100. The costs were significantly different between the two groups, CAD dollars 185.90 (+/-26.37, 95% CI, CAD dollars 173,89; CAD dollars 197.90) and CAD dollars 232.90 (+/-CAD dollars 66.84, 95% CI, CAD dollars 198.53; CAD dollars 267.27) per patient for ondansetron and dimenhydrinate, respectively. The length of stay in the postanesthetic care unit (PACU) represented over 97% of total costs, and the mean lengths of stay in the PACU for ondansetron and dimenhydrinate were significantly different, 3.43 and 4.41 h, respectively. CONCLUSION: This study should serve as a pilot for a large-scale investigation on the correlation between the length of stay in the PACU and the antiemetic agent used.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".