Perspective Chapter: Microfluidic Technologies for On-Site Detection and Quantification of Infectious Diseases – The Experience with SARS-CoV-2/COVID-19
Bibliographic record
Abstract
Over the last 2 years, the economic and infrastructural damage incurred by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exposed several limitations in the world’s preparedness for a pandemic-level virus. Conventional diagnostic techniques that were key in minimizing the potential transmission of SARS-CoV-2 were limited in their overall effectiveness as on-site diagnostic devices due to systematic inefficiencies. The most prevalent of said inefficiencies include their large turnaround times, operational costs, the need for laboratory equipment, and skilled personnel to conduct the test. This left many people in the early stages of the pandemic without the means to test themselves readily and reliably while minimizing further transmission. This unmet demand created a vacuum in the healthcare system, as well as in industry, that drove innovation in several types of diagnostic platforms, including microfluidic and non-microfluidic devices. In this chapter, we will explore how integrated microfluidic technologies have facilitated the improvements of previously existing diagnostic platforms for fast and accurate on-site detection of infectious diseases.
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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 | 0.000 | 0.000 |
| 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".