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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".