Prevalence of Zoonotic or Potentially Zoonotic Bacteria, Antimicrobial Resistance, and Somatic Cell Counts in Organic Dairy Production: Current Knowledge and Research Gaps
Bibliographic record
Abstract
The review's objective was to identify, evaluate, and summarize the findings of all primary research published in English or French, investigating prevalence of zoonotic or potentially zoonotic bacteria, bacterial resistance to antimicrobials, and somatic cell count (SCC) in organic dairy production, or comparing organic and conventional dairy production, using a systematic review methodology. Among 47 studies included in the review, 32 comparison studies were suitable for quality assessment. Fifteen studies were not assessed for quality, due to their descriptive nature or a low sample size (n ≤ 2 farms). Overall, bacterial outcomes were reported in 17 studies, and prevalence of antimicrobial resistance (AMR) and multidrug resistance (MDR) of zoonotic or potentially zoonotic bacteria in 12 and 7 studies, respectively. Campylobacter spp., Escherichia coli including Shiga toxin–producing strains, Salmonella spp., Staphylococcus aureus, and SCC were investigated in 2, 7, 4, 6, and 15 studies, respectively. Contradictory findings were reported for differences in bacterial outcomes and SCC between dairy production types (organic vs. conventional). Lower prevalence of AMR on organic dairy farms was reported more consistently in studies conducted in the United States, as opposed to those conducted in Europe. These conflicting findings may result from geographic differences in organic production regulations governing antimicrobial usage, use of antimicrobials in conventional dairy production, and baseline prevalence, as well as laboratory methods, study designs, or methods of analysis employed. The majority (four of seven) of MDR investigations reported no significant differences in prevalence. Overall, only 9 of 32 studies met all five methodological soundness criteria. More well designed, executed, and reported primary research is needed at the farm and post-farm levels.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".