Antimicrobial Stewardship Programs: Appropriate Measures and Metrics to Study their Impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antimicrobial stewardship is a new field that struggles to find the right balance between meaningful and useful metrics to study the impact of antimicrobial stewardship programs (ASPs). ASP metrics primarily measure antimicrobial use, although microbiological resistance and clinical outcomes are also important measures of the impact an ASP has on a hospital and its patient population. Antimicrobial measures looking at consumption are the most commonly used measures, and are focused on defined daily doses, days of therapy, and costs, usually standardized per 1,000 patient-days. Each measure provides slightly different information, with their own upsides and downfalls. Point prevalence measurement of antimicrobial use is an increasingly used approach to understanding consumption that does not entirely rely on sophisticated electronic information systems, and is also replicable. Appropriateness measures hold appeal and promise, but have not been developed to the degree that makes them useful and widely applicable. The primary reason why antimicrobial stewardship is necessary is the growth of antimicrobial resistance. Accordingly, antimicrobial resistance is an important metric of the impact of an ASP. The most common approach to measuring resistance for ASP purposes is to report rates of common or important community- or nosocomial-acquired antimicrobial-resistant organisms, such as methicillin-resistant Staphylococcus aureus and Clostridium difficile. Such an approach is dependent on detection methods, community rates of resistance, and co-interventions, and therefore may not be the most accurate or reflective measure of antimicrobial stewardship interventions. Development of an index to reflect the net burden of resistance holds theoretical promise, but has yet to be realized. Finally, programs must consider patient outcome measures. Mortality is the most objective and reliable method, but has several drawbacks. Disease- or organism-specific mortality, or cure, are increasingly used metrics.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it