Isopropyl alcohol as anti-emetic therapy in the emergency department: study protocol for a multi-center randomized controlled trial
Bibliographic record
Abstract
Background: Nausea and vomiting is a common and distressing presenting complaint in Emergency Departments (EDs). There is no definite evidence to support the superiority of any anti-emetic therapy over another, or over placebo. Identification of an effective anti-emetic therapy in the ED setting with minimal side effects would be of great benefit. Isopropyl alcohol inhalation has been reported to be an effective treatment for post-operative nausea and vomiting, with no reported adverse events. The objective of this study is to determine if nasally inhaled isopropyl alcohol swabs are effective in alleviating nausea and/or vomiting in patients presenting to the ED with a chief complaint of nausea and/or vomiting. Methods: We will conduct a randomized, controlled, multicenter trial with three subject arms: 1) nasally inhaled isopropyl alcohol swabs every 10 minutes for a total of one hour, 2) nasally inhaled isopropyl alcohol swabs every 20 minutes for a total of one hour, or 3) no intervention. 135 participants ≥18 years old and presenting to the ED with a chief complaint of nausea and/or vomiting with a level of 3 or greater on a verbal numeric rating scale (NRS) will be recruited for a duration of two hours. The primary outcome measure is the mean reduction in nausea scores comparing the pre-intervention score to the lowest post-intervention nausea score. The secondary outcome measures will be participant satisfaction scores using a verbal NRS, receipt of any rescue anti-emetic medications, ED length of stay, and participant disposition (admission or discharge home). Discussion: This study will determine the efficacy of inhaled isopropyl alcohol swabs by determining the optimal dosing frequency achieving adequate anti-emetic action. This has the potential to guide future triage protocols to incorporate this therapy to provide earlier symptomatic relief to patients, and also has the potential to prevent morbidity suffered by patients in the emergency department and improving patient satisfaction and efficiently use in-patient resources. We strongly suspect that once this study is performed, it will be useful for ED physicians in treating nausea and vomiting in the ED.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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".