Auditing tools for antimicrobial prescribing in solid organ transplant recipients: The why, the how, and an assessment of current options
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship (AMS) aims to optimize antimicrobial use. Auditing and reporting of antimicrobial prescribing are essential. Auditing tools for solid organ transplant (SOT) patients should tailor to their needs. METHODS: We reviewed published data describing auditing tools in the general and SOT population. RESULTS: We focused on three internationally or nationally available auditing tools. The National Antimicrobial Prescribing Survey (NAPS) is web-based tool to report antimicrobial consumption and assess appropriateness using standardized definitions based on consensus guidelines. In the absence of guidelines, adjudication is based on AMS principles. An automated dashboard, analyses by indication or antimicrobial, and benchmarking reports are available. The National Healthcare Safety Network Antimicrobial Use/Resistance module was developed by the Centers for Disease Control and Prevention for hospitals to upload monthly data, which are standardized for benchmarking. It does not assess appropriateness or address SOT wards. The Global-Point Prevalence Survey from bioMérieux collects data on antimicrobial regimen, indication and microbial resistance. Variables unique to SOT include comorbidities and devices. Assessment of appropriateness is limited to guideline adherence, and benchmarking may require prearrangement with bioMérieux. Benchmarking requires prearrangement. Advances in electronic health record systems and clinical decision support tools can improve the efficiency of the auditing process. CONCLUSION: Each AMS auditing tool has unique features for SOT patients. Capturing immunosuppression, source control, organ dysfunction, donor-derived infection, serology, and colonization status will enhance their applicability.
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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 | 0.081 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".