Estimation of defined daily doses of antimicrobials for dogs and cats treated for bacterial cystitis.
Bibliographic record
Abstract
Objective: To calculate prescribed daily doses (PDDs) for selected antimicrobials and evaluate application of defined daily doses (DDDs) using an antimicrobial purchasing dataset. Animals: Data from dogs and cats treated for bacterial cystitis at a veterinary practice network were evaluated. Procedure: cats. Results: PDDs for dogs and cats were determined and adjusted DDDs were calculated and applied to an antimicrobial purchasing dataset from 886 veterinary clinics, demonstrating the difference between mass-based and DDD data. Conclusions: DDDs can be estimated using prescription datasets, accounting for differences in weights (between and within species) and relative use between dogs and cats. These can be applied to broader (sales, purchase) datasets to provide a more detailed understanding of how antimicrobials are used. Clinical relevance: DDDs could be a useful measure for assessing mass-based antimicrobial use datasets as part of antimicrobial stewardship surveillance efforts.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".