Moving beyond metrics: Capturing the clinical context behind antibiotic prescriptions in French broiler production
Bibliographic record
Abstract
Significant reductions in antimicrobial use (AMU) in food production animals have been observed over the last 10 years across Europe. We sought to understand recent changes in AMU by characterising antibiotic prescribing patterns in poultry production in the context of associated clinical information. We analysed trends in AMU for conventional broiler chicken production in France based on a dataset of 193,526 sales for 33,831 flocks on 2,120 farms for 2015-2023, including 21,218 antibiotic prescriptions. We found the percentage of flocks prescribed antibiotics dropped from 65% in 2013 to 20% in 2023, plateauing in 2020-2023 (oscillating between 13% and 23%), and observed a reduction in the use of critical antibiotics. A multiple correspondence analysis and hierarchical clustering on principal components of 1112 antibiotic prescriptions and associated clinical data for 2021-22 produced 1940 prescription events, grouped in five clusters of antibiotic prescribing patterns, each characterised by a combination of clinical indicators related to age at treatment, lesions, syndromes, diagnoses, and isolated bacteria. Two main clusters were associated with bacterial diagnoses, suggesting that use of antibiotics in these clusters was necessary to manage disease. Two clusters were identified as potential targets for further interventions to improve antimicrobial stewardship, focusing on underlying factors driving AMU rather than outright reductions. Our findings raise questions about the sustainability of further reductions in AMU and their implications for animal health and welfare. This calls for a shift to a more sustainable approach to monitoring antimicrobial stewardship, using integrated indicators which consider AMU within its broader context.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".