Influence of Standardized Orders on Postoperative Nausea and Vomiting after Gynecologic Surgery
Bibliographic record
Abstract
OBJECTIVE: The risk of postoperative nausea and vomiting (PONV) after gynecologic surgery remains high, despite effective prophylactic medications. Thus, the objectives of this study were to determine whether standardized orders for the prophylaxis and treatment of PONV in gynecologic surgery patients (1) reduce PONV occurrence, (2) reduce total costs, and (3) influence the choice of medications used for PONV prophylaxis and treatment. METHODS: A retrospective design was employed in which a random sample of 200 patients was selected from each of the two 6-month phases before (pre) and after (post) the implementation of standardized orders for PONV prophylaxis and treatment. The primary outcome was the occurrence of any PONV episode. Logistic regression was used to adjust for potential confounding factors. All costs were in 1999 Canadian dollars (Canadian dollar = US$0.673 in 1999). RESULTS: Characteristics were similar except for surgical and anesthesia length between phases. The proportion of patients who received PONV prophylaxis increased from 31% (pre) to 47% (post; p = 0.002). There was a reduction in the risk of a PONV event in the post-phase (odds ratio [OR] 0.67, 95% CI 0.67 to 0.97; p = 0.04). The risk of PONV was significantly reduced with the administration of prophylactic medications (OR 0.46, 95% CI 0.46 to 0.67; p = 0.001). There was a reduction in the mean number of PONV episodes in the post-phase (1.47 events) versus the pre-phase (1.81 events; p = 0.02). A reduction in mean PONV management costs was observed in the post-phase ($8.31, SD +/- 8.50) compared with the pre-phase ($10.23, SD +/- 8.25; p = 0.02). For mean prophylactic costs, these were significantly higher in the postimplementation phase compared with the preimplementation phase ($1.64, SD +/- 3.36 vs. $0.91, SD +/- 2.43; p = 0.013). For mean total PONV costs (prophylaxis plus management costs), there was a nonsignificant reduction in the postimplementation phase compared with the preimplementation phase ($9.95, SD +/- 9.20 vs. $11.15, SD +/- 8.51, respectively; p = 0.18). Univariate sensitivity analyses revealed that the economic results were sensitive to several parameters. CONCLUSIONS: The implementation of preprinted order forms for PONV prophylaxis and treatment appears to be an effective and economically attractive strategy.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".