Metrics for evaluating antibiotic use and prescribing in outpatient settings
Bibliographic record
Abstract
Antimicrobial stewardship interventions in outpatient settings are diverse and a variety of outcomes have been used to evaluate these efforts. This narrative review describes, compares and provides specific examples of antibiotic use and other prescribing measures to help antimicrobial stewards better understand, interpret and implement metrics for this setting. A variety of data have been used including those generated from drug sales, prescribing and dispensing activities, however data generated closest to when an individual patient consumes an antibiotic is usually more accurate for estimating antibiotic use. Availability of data is often dependent on context such as information technology infrastructure and the healthcare system under consideration. While there is no ideal antibiotic use or prescribing metric for evaluating antimicrobial stewardship activities in the outpatient setting, the intervention of interest and available data sources are important factors. Common metrics for estimating antimicrobial use include DDD per 1000 inhabitants per day (DID) and days of therapy per 1000 inhabitants/day (DOTID). Other prescribing metrics such as antibiotic prescribing rate (APR), proportion of prescriptions containing an antibiotic, proportion of prolonged antibiotic courses prescribed, estimated appropriate APR and quality indicators are used to assess specific aspects of antimicrobial prescribing behaviour such as initiation, selection, duration and appropriateness. Understanding the context of prescribing practices helps to ensure feasibility and relevance when implementing metrics and targets for improvement in the outpatient setting.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".