Portable point-of-care diagnostic devices: an updated review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it