Establishing obstetrics-specific metrics and interventions for antimicrobial stewardship
Bibliographic record
Abstract
Background: To describe baseline antimicrobial stewardship (AMS) metrics and apply AMS interventions in an inpatient obstetrical population. Methods: From October 2018 to October 2019, our tertiary-care obstetrical center reviewed components of our AMS program, which included: (1) antimicrobial consumption data, (2) point prevalence surveys (PPS), and (3) prospective audit and feedback. We reviewed institutional data for antimicrobial consumption from the pharmacy database. Detailed point prevalence surveys were conducted for all antimicrobial prescriptions on two predefined dates each month. Daily audits and feedback assessed the appropriateness of all non-protocolized antimicrobials. Results: Our average antimicrobial length of therapy (LOT) was 12 days per 100 patient-days, where erythromycin (2.33), amoxicillin (2.28), and ampicillin (1.81) were the greatest contributors. Point prevalence surveys revealed that 28.8% of obstetrical inpatients were on antimicrobials, of which 11.2% were inappropriate. Protocolized antimicrobials were 62% less likely ( p = 0.027) to be inappropriate. From 565 audited prescriptions, 110 (19.5%) resulted in feedback, where 90% of recommendations were accepted and implemented. The most common reasons for interventions include incorrect dosage, recommending a diagnostic test before continuing antimicrobials, and changing antimicrobials based on specific culture and sensitivity. Conclusions: Antimicrobial use in obstetrics is unique compared to general inpatients. We provide a baseline set of metrics for AMS at our obstetrical center intending to lay the groundwork for AMS programming in our discipline. Antimicrobial protocolization, as well as audit and feedback, are feasible interventions to improve antimicrobial prescribing patterns.
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".