A RANDOMIZED TRIAL TO MEASURE THE OPTIMAL ROLE OF THE PHARMACIST IN PROMOTING EVIDENCE-BASED ANTIBIOTIC USE IN ACUTE CARE HOSPITALS
Bibliographic record
Abstract
BACKGROUND: There is a considerable gap between randomized clinical trials and implementing the results into practice. This is particularly relevant in the use of broad-spectrum antibiotics in hospitals. Hospital pharmacists can be effective vehicles for bridging this gap and promoting evidence-based medicine. To determine the most effective way of using the pharmacist in this role, a prospective cefotaxime intervention study was conducted with randomization incorporated into the design as well as patient-related therapeutic outcomes. METHODS: A total of 323 patients who were prescribed cefotaxime were randomized into an intervention or nonintervention group where only the former was challenged by pharmacists for inappropriate cefotaxime usage relative to hospital guidelines. The primary outcome was the appropriateness of cefotaxime prescribing between groups. Logistic regression analysis was then used to identify factors that were associated with successful clinical response. RESULTS: Overall, 94% of orders in the intervention group met cefotaxime dosage criteria compared with 86% in the control group (p = .018). However, there was no impact with respect to promoting cefotaxime use for an appropriate indication (81% vs. 80%; p = .67). There was a trend for improved clinical outcomes in patients who received cefotaxime within hospital guidelines (OR = 1.73; p = .31). CONCLUSIONS: The pharmacist as a vehicle for promoting the appropriate use of broad-spectrum antibiotics in the acute care hospital setting can improve the dosing of such agents. However, several barriers to optimizing the impact of the pharmacist were implied by the data. Removing these barriers could increase the pharmacists' utility as an agent for improved patient care.
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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 | 0.016 | 0.033 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".