Validation of the Antifungal National Antimicrobial Prescribing Survey (AF-NAPS) quality assessment tool
Bibliographic record
Abstract
BACKGROUND: The Antifungal National Antimicrobial Prescribing Survey (AF-NAPS) was developed to undertake streamlined quality audits of antifungal prescribing. The validity and reliability of such tools is not characterized. OBJECTIVES: To assess the validity and reliability of the AF-NAPS quality assessment tool. METHODS: Case vignettes describing antifungal prescribing were prepared. A steering group was assembled to determine gold-standard classifications for appropriateness and guideline compliance. Infectious diseases physicians, antimicrobial stewardship (AMS) and specialist pharmacists undertook a survey to classify appropriateness and guideline compliance of prescriptions utilizing the AF-NAPS tool. Validity was measured as accuracy, sensitivity and specificity compared with gold standard. Inter-rater reliability was measured using Fleiss' kappa statistics. Assessors' responses and comments were thematically analysed to determine reasons for incorrect classification. RESULTS: Twenty-eight clinicians assessed 59 antifungal prescriptions. Overall accuracy of appropriateness assessment was 77.0% (sensitivity 85.3%, specificity 68.0%). Highest accuracy was seen amongst specialist (81%) and AMS pharmacists (79%). Prescriptions with lowest accuracy were in the haematology setting (69%), use of echinocandins (73%), mould-active azoles (75%) and for prophylaxis (71%). Inter-rater reliability was fair overall (0.3906), with moderate reliability amongst specialist pharmacists (0.5304). Barriers to accurate classification were incorrect use of the appropriateness matrix, knowledge gaps and lack of guidelines for some indications. CONCLUSIONS: The AF-NAPS is a valid tool, assisting assessors to correctly classify appropriate prescriptions more accurately than inappropriate prescriptions. Specialist and AMS pharmacists had similar performance, providing confidence that both can undertake AF-NAPS audits to a high standard. Identified reasons for incorrect classification will be targeted in the online tool and educational materials.
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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 | 0.130 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".