Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review
Bibliographic record
Abstract
Rapid laboratory tests are urgently required to inform antimicrobial use in food animals. Our objective was to synthesize knowledge on the direct application of long-read metagenomic sequencing to respiratory samples to detect bacterial pathogens and antimicrobial resistance genes (ARGs) compared to PCR, loop-mediated isothermal amplification, and recombinase polymerase amplification. Our scoping review protocol followed the Joanna Briggs Institute and PRISMA Scoping Review reporting guidelines. Included studies reported on the direct application of these methods to respiratory samples from animals or humans to detect bacterial pathogens ±ARGs and included turnaround time (TAT) and analytical sensitivity. We excluded studies not reporting these or that were focused exclusively on bioinformatics. We identified 5,636 unique articles from 5 databases. Two-reviewer screening excluded 3,964, 788, and 784 articles at 3 levels, leaving 100 articles (19 animal and 81 human), of which only 7 studied long-read sequencing (only 1 in animals). Thirty-two studies investigated ARGs (only one in animals). Reported TATs ranged from minutes to 2 d; steps did not always include sample collection to results, and analytical sensitivity varied by study. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. There is an opportunity to harness the rapid development in this space to detect multiple pathogens and ARGs on a single sequencing run. Long-read metagenomic sequencing tools show potential to address the urgent need for research into rapid tests to support antimicrobial stewardship in food animal production.
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".