Enhancing antimicrobial stewardship program: impact of clinical pharmacist-driven feedback in the absence of infectious diseases physicians—a multicenter quasi-experimental study
Bibliographic record
Abstract
Objective: To evaluate the impact of clinical pharmacist-driven feedback on Antimicrobial Stewardship Program (AMSP) in the absence of infectious disease physicians across three different geographic locations. Design: Multicenter quasi-experimental study. Setting: Three private tertiary referral centers in different geographical locations in India. Participants: All consecutive prescriptions with restricted antibiotics for inpatients during the study period. Intervention: This study was conducted over 15 months from June 2022 to May 2023. The impact of mentoring clinical pharmacists by infectious disease physicians, enhancing their communication abilities for providing proactive feedback, and the impact on prescription practice were measured in terms of new prescriptions of restricted antibiotics, compliance to clinical pharmacist advice, and the duration of restricted antibiotic therapy usage, measured in terms of days of therapy (DOT) of restricted antibiotics. Gross mortality was reviewed as a balancing measure, and dose/dosing errors were considered as a secondary outcome. Data were captured in Microsoft Excel and analyzed using the SPSS software. Results: Clinical pharmacist-led antimicrobial stewardship interventions were found to have a significant impact on decreasing antibiotic prescriptions, increasing healthcare organization policy compliance, and decreasing DOT for restricted antibiotics. Culture sampling, acceptance of antimicrobial stewardship advice, dosing errors, or mortality rates were not statistically significantly related to the other study parameters. Conclusion: Clinical pharmacist-driven AMSP can be effectively implemented irrespective of the cultural and geographical setting due to their ability to improve prescription practices.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".