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Record W4415063249 · doi:10.1111/zph.70017

Colombian Standards for Antimicrobial Dosing in Cattle: Establishing Defined Daily Doses and Defined Course Doses

2025· article· en· W4415063249 on OpenAlexaboutno aff
Brahian Camilo Tuberquia‐López, Nathalia M. Correa‐Valencia

Bibliographic record

VenueZoonoses and Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsnot available
Fundersnot available
KeywordsDosingAntimicrobialComparabilityAntimicrobial drugAntibioticsDrug administration

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.350
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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