An Overview of Emerging Point-of-Care Tests for Differentiating Bacterial and Viral Infections
Bibliographic record
Abstract
Antimicrobial resistance is an important health concern in Canada and around the world. Although resistance arises naturally, the overuse of antibiotics, among many other behavioural, social, and economic drivers, contributes to the emergence of resistance patterns. Within health care settings, diagnostic uncertainty, a situation in which it is uncertain whether a suspected infection is due to a bacterial, viral, or other microorganism, is a regarded as a key driver that contributes to overuse of antibiotics. In these situations, antibiotics may be prescribed although the infection is viral. Emerging health technologies that can help reduce diagnostic uncertainty of acute infections at the point of care may help reduce the unnecessary use of antibiotics. If these point-of-care diagnostic devices demonstrate clinical benefit and cost-effectiveness for health systems, they may complement other interventions as part of antibiotic stewardship programs. This Horizon Scan provides an overview of new and emerging point-of-care tests that help differentiate bacterial and viral infections. Although rapid tests for identifying specific pathogens have existed for decades, these emerging tests aim to assess a wider range of possible pathogens and help inform treatment decisions. Different types of emerging devices, such as rapid molecular tests and immunoassays, are described including how they work and information about their capabilities that may influence their potential use. The report also describes the evidence about the diagnostic accuracy of certain tests and their effect on reducing antibiotic prescribing. Considerations are provided about where tests might be beneficial, such as primary care settings, and the emerging evidence base for their feasibility and acceptability. The emerging evidence suggests that point-of-care tests could be effective tools as part of antibiotic stewardship programs, but further studies assessing specific devices in randomized controlled trials are recommended by researchers and health technology assessment agencies. Monitoring the continued development of devices and the testing landscape, especially in post-pandemic health care, will be important for decision-makers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".