Evaluating the Diagnostic Performance of Long-Read Metagenomic Sequencing Compared to Culture and Antimicrobial Susceptibility Testing for Detection of Bovine Respiratory Bacteria and Indicators of Antimicrobial Resistance
Bibliographic record
Abstract
Background/Objectives: Long-read metagenomic sequencing can detect bacteria and antimicrobial resistance genes (ARGs) from bovine respiratory samples, providing an alternative to culture and antimicrobial susceptibility testing (C/S). This study applied Bayesian latent class models (BLCMs) to estimate the sensitivity (Se) and specificity (Sp) of long-read metagenomic sequencing compared to C/S for detecting Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni, as well as associated macrolide and tetracycline resistance potential. Methods: Deep nasopharyngeal swabs were collected from fall-placed feedlot calves at arrival, 13, and 36 days on feed across two years and two metaphylaxis protocols. Samples underwent C/S and long-read metagenomic sequencing. BLCMs were used to estimate Se and Sp for the detection of bacteria and potential for antimicrobial resistance (AMR). Results: Se and Sp for detecting respiratory bacteria by metagenomics were not significantly different than culture, with four exceptions. For the 2020 samples, Se for M. haemolytica was lower than culture, and Sp for H. somni was lower, while in both 2020 and 2021 samples, Se for P. multocida was higher for metagenomics than culture. The estimated Se and Sp of metagenomics for the detection of msrE-mphE, EstT, and tet(H) within bacterial reads were either not significantly different or were lower than AST, with Sp > 95% with one exception. Conclusions: This study provided BLCM-based estimates of clinical Se and Sp of metagenomics and C/S without assuming a gold standard in a large pen research setting. These findings demonstrate the potential of long-read metagenomics to support bovine respiratory disease diagnostics, AMR surveillance, and antimicrobial stewardship in feedlot cattle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".