Scoping review to identify potential non-antimicrobial interventions to mitigate antimicrobial resistance in commensal enteric bacteria in North American cattle production systems
Bibliographic record
Abstract
A scoping review was conducted to identify modifiable non-antimicrobial factors to reduce the occurrence of antimicrobial resistance in cattle populations. Searches were developed to retrieve peer-reviewed published studies in animal, human and in vitro microbial populations. Citations were retained when modifiable non-antimicrobial factors or interventions potentially associated with antimicrobial resistance were described. Studies described resistance in five bacterial genera, species or types, and 40 antimicrobials. Modifiable non-antimicrobial factors or interventions ranged widely in type, and the depth of evidence in animal populations was shallow. Specific associations between a factor or intervention with antimicrobial resistance in a population (e.g. associations between organic systems and tetracycline susceptibility in E. coli from cattle) were reported in a maximum of three studies. The identified non-antimicrobial factors or interventions were classified into 16 themes. Most reported associations between the non-antimicrobial modifiable factors or interventions and antimicrobial resistance were not statistically significant (P > 0·05 and a confidence interval including 1), but when significant, the results were not consistent in direction (increase or decrease in antimicrobial resistance) or magnitude. Research is needed to better understand the impacts of promising modifiable factors or interventions on the occurrence of antimicrobial resistance before any recommendations can be offered or adopted.
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 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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".