Practice Spotlight: Pharmacists in an Antimicrobial Stewardship Program
Bibliographic record
Abstract
T hree years ago, the Infectious Diseases Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA) published guidelines for developing an institutional antimicrobial stewardship program. 1 One impetus for these guidelines was emerging evidence that many hospitals do not actively work to ensure that antimicrobials are used appropriately, despite well-described problems with antimicrobial resistance and nosocomial infections with organisms that have been linked to antimicrobial overuse, such as Clostridium difficile.Another reason for developing and disseminating antimicrobial stewardship guidelines was the paucity of new antimicrobials in the drug development pipeline, as documented by the IDSA. 2 Antimicrobial stewardship is the practice of using antimicrobials appropriately to optimize patient outcomes, minimize costs, and avoid the adverse effects and collateral damage associated with these drugs.The IDSA-SHEA guidelines recommend that the core members of an antimicrobial stewardship program include (but not be limited to) an infectious diseases physician and a pharmacist with infectious diseases training and that the program be appropriately supported by the institution's senior administration. 1The guidelines also recommend prospective audit (with interventions and feedback) and restriction and preauthorization of the formulary as key stewardship activities. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".