Nonculture Molecular Techniques for Diagnosis of Bacterial Disease in Animals
Bibliographic record
Abstract
The past decade has seen remarkable technical advances in infectious disease diagnosis, and the pace of innovation is likely to continue. Many of these techniques are well suited to pathogen identification directly from pathologic or clinical samples, which is the focus of this review. Polymerase chain reaction (PCR) and gene sequencing are now routinely performed on frozen or fixed tissues for diagnosis of bacterial infections of animals. These assays are most useful for pathogens that are difficult to culture or identify phenotypically, when propagation poses a biosafety hazard, or when suitable fresh tissue is not available. Multiplex PCR assays, DNA microarrays, in situ hybridization, massive parallel DNA sequencing, microbiome profiling, molecular typing of pathogens, identification of antimicrobial resistance genes, and mass spectrometry are additional emerging technologies for the diagnosis of bacterial infections from pathologic and clinical samples in animals. These technical advances come, however, with 2 caveats. First, in the age of molecular diagnosis, quality control has become more important than ever to identify and control for the presence of inhibitors, cross-contamination, inadequate templates from diagnostic specimens, and other causes of erroneous microbial identifications. Second, the attraction of these technologic advances can obscure the reality that medical diagnoses cannot be made on the basis of molecular testing alone but instead through integrated consideration of clinical, pathologic, and laboratory findings. Proper validation of the method is required. It is critical that veterinary diagnosticians understand not only the value but also the limitations of these technical advances for routine diagnosis of infectious disease.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".