Assessment of Antimicrobial Utilization Metrics: Days of Therapy Versus Defined Daily Doses and Pharmacy Dispensing Records Versus Nursing Administration Data
Bibliographic record
Abstract
OBJECTIVE: To compare antimicrobial utilization data derived from pharmacy dispensing records and nursing administration record data by 2 commonly used units of measure. DESIGN, PARTICIPANTS, AND METHODS: Data from nursing administration records and pharmacy dispensing records were obtained for 32 medical wards. From nursing and pharmacy data, defined daily doses (DDD) were calculated, and from the nursing data, days of therapy were derived. Direct comparison of total antimicrobial use was performed by graphical analysis and linear regression. Slope of trend line was used to quantify the difference between pairs of measures. Bland-Altman plots were constructed to determine constant and proportional bias. At the level of individual agents, difference between pairs of measures was calculated and presented graphically and the average (95% CI) for the difference between measures was determined. RESULTS: Nursing administration record-derived DDD were on average 23% lower than corresponding rates of pharmacy dispensing record-derived DDD. The difference between rates of utilization by days of therapy vs DDD from the same source (nursing) was relatively small. Results from analysis of different individual agents were highly variable with wide 95% CIs. CONCLUSIONS: In our setting, we found clinically relevant differences in antimicrobial utilization associated with data from different sources. This outweighed the importance of the metric (DDD or days of therapy). However, measurement of use of individual agents was highly variable and sensitive to both metric unit and data sources.
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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 | 0.049 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".