Trends in Antimicrobial Consumption May Be Affected by Units of Measure
Bibliographic record
Abstract
To the Editor—A recent article by Polk et al. [1] assessed the discrepancies between measures of antimicrobial consumption—specifically, defined daily doses (DDD) and days of therapy—in a sample of 130 US hospitals over a 1-year period. Those who are familiar with drug consumption studies and their methods are well aware that calculation of DDD is an attempt to estimate actual days of therapy, with the recognition that there is discrepancy but that it is likely minor overall. Others, including ourselves, have also attempted to improve on DDD measures and quantify this discrepancy [2,3,4–5]. By accessing patient records, Polk et al. [1] were able to compare the estimate with the gold standard. Although there was not a statistically significant difference observed in overall systemic antibacterial use between the 2 measures, with 6 out of 10 of the commonly used individual antibacterial agents, the difference was significant and was considered to be of major or moderate importance.
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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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".