Quality Metrics for Antimicrobial Stewardship Programs
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship programs (ASPs) are targeted to optimize antimicrobial use. However, pediatric metrics used to measure outcomes of ASPs are not well established. Our aim for this project was to identify, refine, and develop consensus on standard metrics for pediatric ASPs. METHODS: By using a modified Delphi process, 2 surveys were sent to experts and stakeholders to establish consensus on the utility of metrics. These were subdivided into 4 ASP domains: (1) antimicrobial consumption, (2) microbiologic outcomes, (3) clinical outcomes, and (4) process measures. Respondents were asked to rank the scientific merit, impact, feasibility, and accountability of each metric. Metrics with ≥75% agreement for scientific merit were included and metrics with ≤25% agreement were discarded. Consensus was finalized with a face-to-face meeting and final survey. RESULTS: Thirty-eight participants from 15 pediatric hospitals across Canada completed all 3 rounds of the Delphi survey. In the domain of antimicrobial consumption, the 2 selected metrics were (1) days of therapy per 1000 patient-days and (2) total antimicrobial days. The clinical and process outcomes chosen were (1) 30-day readmission rate and (2) adherence to ASP recommendations, respectively. A microbiologic outcome was felt to be important and feasible, but consensus could not be obtained on a measure. Several barriers to implementation of the metrics were identified, including information technology limitations at various centers. CONCLUSIONS: We obtained consensus on 4 metrics to evaluate pediatric antimicrobial stewardship activities in Canada. Adoption of these metrics by pediatric ASPs will facilitate measurement of outcomes nationally and internationally.
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 | 0.144 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".