Documenting the indication for antimicrobial prescribing: a scoping review
Bibliographic record
Abstract
BACKGROUND: Documenting an indication when prescribing antimicrobials is considered best practice; however, a better understanding of the evidence is needed to support broader implementation of this practice. OBJECTIVES: We performed a scoping review to evaluate antimicrobial indication documentation as it pertains to its implementation, prevalence, accuracy and impact on clinical and utilisation outcomes in all patient populations. ELIGIBILITY CRITERIA: Published and unpublished literature evaluating the documentation of an indication for antimicrobial prescribing. SOURCES OF EVIDENCE: A search was conducted in MEDLINE, Embase, CINAHL and International Pharmaceutical Abstracts in addition to a review of the grey literature. CHARTING AND ANALYSIS: Screening and extraction was performed by two independent reviewers. Studies were categorised inductively and results were presented descriptively. RESULTS: We identified 123 peer-reviewed articles and grey literature documents for inclusion. Most studies took place in a hospital setting (109, 89%). The median prevalence of antimicrobial indication documentation was 75% (range 4%-100%). Studies evaluating the impact of indication documentation on prescribing and patient outcomes most commonly examined appropriateness and identified a benefit to prescribing or patient outcomes in 17 of 19 studies. Qualitative studies evaluating healthcare worker perspectives (n=10) noted the common barriers and facilitators to this practice. CONCLUSION: There is growing interest in the importance of documenting an indication when prescribing antimicrobials. While antimicrobial indication documentation is not uniformly implemented, several studies have shown that multipronged approaches can be used to improve this practice. Emerging evidence demonstrates that antimicrobial indication documentation is associated with improved prescribing and patient outcomes both in community and hospital settings. But setting-specific and larger trials are needed to provide a more robust evidence base for this practice.
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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 | 0.060 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.041 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".