STRATEGIES TO OPTIMIZE ANTIBIOTIC STEWARDSHIP IN THE OUTPATIENT PEDIATRIC POPULATION: A REVIEW OF THE LITERATURE
Bibliographic record
Abstract
Introduction: About one-third of all antimicrobial prescriptions are clinically unnecessary and potentially harmful in the outpatient pediatric setting, which is an area that commonly features physician assistants prescribe medications. Behavioural factors were found to influence prescription rates. Purpose of Review: The purpose of this literature review is to present a comprehensive overview of the best available evidence regarding the effectiveness of strategies to optimize prescribing behaviours in outpatient pediatric settings. Methods: 23 articles pertained to five individual stewardship strategies and bundled interventions. Search terms were created from four main categories. Databases were searched for articles by title and abstract for highlighting pediatrics/adolescents, outpatient prescription rates, and effectiveness of antibiotic stewardship strategies. 39 articles were found and 19 articles were chosen for review. Older studies (pre-2017) and those with adult samples were included if recent literature on a strategy was scarce and/or datasets were large. Discussion: Bundled and non-bundled approaches were associated with better first-line antibiotic prescribing, reduced broad-spectrum antibiotic prescribing, reduced prescribed antibiotic durations, increased delayed antibiotic prescriptions, and sustained effects in studies with postintervention periods. However, multiple studies did not utilize a pediatric sample and may limit its generalizability. Conclusion: By engaging in more than one behavioural strategy to combat unnecessary antimicrobial prescriptions, physician assistants in outpatient pediatric settings will be better able to adhere to the judicious use of antimicrobial prescriptions. More research should be undertaken that is specific to a pediatric population, within the Canadian context, and involving physician assistants employed in the community.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".