184. Pharmacist role in antimicrobial stewardship research: a 30-year experience
Bibliographic record
Abstract
Abstract Background Antimicrobial stewardship program (ASP) guidance from the Centers for Disease Control and Prevention recommends co-leadership of both an infectious-diseases (ID) physician and ID-trained pharmacist. Pharmacists play a key role in the therapeutic management, administration, and implementation of ASP interventions. The purpose of this study, conducted on behalf of the Society of Infectious Diseases Pharmacists, was to describe the involvement of pharmacists in publications of ASP interventional research. Methods A PubMed search was conducted to identify publications in the United States and Canada from 1990–2019 including “antimicrobial (or antibiotic) stewardship” or “antimicrobial (or antibiotic) intervention.” Articles were screened for active interventions with comparator arms. A random subset of 100 pharmacist-authored manuscripts were selected using a time-based clustering strategy to review specific study designs, populations, interventions, and endpoints. Results Of 1,426 publications, 340 met inclusion. Two-thirds (228/340) of all interventional antimicrobial stewardship studies included a pharmacist author. Pharmacists were lead authors in 59% (135/228) of studies that included a pharmacist. Among the randomized subset of pharmacist-authored manuscripts (n=100), the average impact factor of journals with pharmacists as the first author was 3.52, compared to 5.25 as middle authors. Most studies were inpatient focused (89%), included adults (81%), and conducted in a single-site setting (84%). Pediatrics, immunocompromised, post-acute care, and ambulatory populations comprised less than 10% of the publications. The most common interventions described were audit and feedback (55%), guideline implementation (49%), and education (40%). Endpoints included drug utilization (66%), clinical outcomes (57%), safety events (46%), cost (40%), and appropriateness of therapy (35%). Figure 1. Conclusion Pharmacists have an integral role in publication and dissemination of ASP research. Opportunities exist in multi-site collaboration as well as research in ambulatory, pediatric, and immunocompromised groups. Future research endpoints should be practical, generalizable, and patient-centered. Disclosures Kelly E. Pillinger, PharmD, BCIDP, Pharmacy Times (Other Financial or Material Support, Speaker) Haley Appaneal, PharmD, Shionogi (Grant/Research Support)
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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 | 0.092 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".