Colombian Standards for Antimicrobial Dosing in Cattle: Establishing Defined Daily Doses and Defined Course Doses
Bibliographic record
Abstract
INTRODUCTION: Despite international efforts to monitor antimicrobial (AAM) use, gaps persist, especially in Colombia's livestock sector. Therefore, this study aims to assign Defined Daily Doses (DDDCo) and Defined Course Doses (DCDCo) for cattle in Colombia. METHODS: A systematic search was performed from the online veterinary products registry database to identify veterinary products containing at least one AAM, marketed in Colombia for use in cattle, between 2023 and 2024. The monograph was retrieved from the label, and standard weights were applied to compute doses if required. DDDCo and DCDCo were assigned by calculating an average of daily and course doses, respectively. Overall, 856 records containing at least one AAM were listed as active for the market. RESULTS: A total of 321 injectable parenteral medications and 32 oral parenteral products were identified. For non-systemic use, the medications included 89 intramammary, 14 intrauterine, and 38 topical formulations. DDDCo and DCDCo values were assigned successfully for each AAM identified by route of administration. CONCLUSIONS: This study systematically assigned DDD and DCD to quantify antibiotic use in Colombian cattle, highlighting 90% comparability with Canada and Europe but noting differences in administration routes, drug combinations, and cattle weight assumptions.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".