Opportunities for Laboratory Testing to Inform Antimicrobial Use for Bovine Respiratory Disease: Application of Information Quality Value Stream Maps in Commercial Feedlots
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The implementation of information quality value stream maps (IQ-VSMs) in food animal production systems can increase our understanding of the opportunities and challenges when using laboratory testing for antimicrobial resistance (AMR) to support antimicrobial stewardship (AMS). Our objectives were to (1) explore the implementation of information quality value stream mapping as a continuous improvement tool to inform decisions for bovine respiratory disease (BRD) management and AMS and (2) apply the information quality dimensions to identified Kaizen opportunities for the integration of laboratory data into BRD management systems to assess the appropriateness of BRD treatment plans in western Canadian feedlot production. METHODS: A 'Current State' IQ-VSM outlined the processes, available information, information processing steps, and control decisions contributing to BRD management and treatment in commercial western Canadian feedlots, recognizing that laboratory BRD pathogens and AMR data are typically not part of BRD management. RESULTS: The 'Future State' IQ-VSM incorporated Kaizen opportunities for improvement, including (i) the strategic collection of respiratory samples from representative samples of calves for laboratory analysis, regardless of clinical BRD status, (ii) compilation of laboratory data at the pen and feedlot levels, and (iii) analysis of pen- and feedlot-level laboratory data to inform the veterinarian's assessment of the appropriateness of current BRD treatment plans. CONCLUSIONS: The IQ-VSMs provided a valuable framework to visualize the integration of BRD pathogen and AMR laboratory data to support AMS and address any potential future testing requirements.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".