Effectiveness of a Pharmacist-Led Intervention to Reduce Acid Suppression Therapy for Stress Ulcer Prophylaxis in ICUs in China: A Multicenter, Stepped-Wedge, Cluster-Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the effectiveness of a pharmacist-led intervention in decreasing the overuse of stress ulcer prophylaxis (SUP) compared with the usual care for adult patients in Chinese ICUs. DESIGN: Pragmatic, multicenter, stepped-wedge, cluster-randomized controlled trial. SETTING: Twenty-six ICUs in China from October 2022 to March 2023. PATIENTS: We enrolled 2199 patients 18 years old or older who were newly admitted to the participating ICUs. INTERVENTIONS: Using the Medical Research Council framework for developing and evaluating complex intervention measures, a multidisciplinary team (Scenarios, Improving and Refining Interventions, Constructing, Refining and Testing Research Theories, Incorporating Stakeholders, Identifying Important Uncertainties, and Economics Considerations) designed a multifaceted intervention. MEASUREMENTS AND MAIN RESULTS: The primary outcomes were the proportion of patients receiving SUP and that with overt gastrointestinal bleeding. We conducted intention-to-treat analyses using generalized linear mixed models to adjust for potential confounders (age, sex, and acute physiology and chronic health evaluation II score) with random effects for the site. The proportion of patients receiving SUP in the intervention group was lower than that in the control group (45.5% vs. 49.5%; odds ratio [OR], 0.81; 95% CI, 0.68-0.96; p = 0.017). The proportion of patients with overt gastrointestinal bleeding was similar (3.7% vs. 4.0%; OR, 1.05; 95% CI, 0.65-2.85; p = 0.81). CONCLUSIONS: The pharmacist-led intervention reduced the proportion of patients receiving SUP in the ICUs, without significantly affecting the proportion of patients with overt gastrointestinal bleeding. These findings will help guide ICU medical decision-making.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".