Portable point-of-care diagnostic devices: an updated review
Bibliographic record
Abstract
The global pandemic caused by the SARS-CoV-2 (COVID) virus indiscriminately impacted people worldwide with unquantifiable and severe impacts on all aspects of our lives, regardless of socioeconomic status. The pandemic brought to light the very real possibility of pathogens changing and shaping the way we live, and our lack of preparedness to deal with viral/bacterial outbreaks. Importantly, the quick detection of pathogens can help prevent and control the spread of disease, making the importance of diagnostic techniques undeniable. Point-of-care diagnostics started as a supplement to standard lab-based diagnostics, and are gradually becoming mainstream. Because of this, and their importance in detecting pathogens (especially in the developing world), their development has accelerated at an unprecedented rate. In this review, we highlight some important and recent examples of point-of-care diagnostics for detecting nucleic acids, proteins, bacteria, and other biomarkers, with the intent of making apparent their positive impact on society and human health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".