Validation of new equipment for SARS-CoV-2 diagnosis in Ecuador: Detection of the virus and antibodies generated by disease and vaccines with one POC device
Bibliographic record
Abstract
The COVID-19 pandemic underscored the critical need to enhance screening capabilities and streamline diagnosis. Point-of-care (POC) tests offer a promising solution by decentralizing testing. We aimed to validate the PLUM device (LSK Technologies Inc.), a portable optical reader, to detect SARS-CoV-2 RNA using direct RT-LAMP targeting the ORF1a and E1 genes and patient antibodies by ELISA. The direct RT-LAMP assays employ nasopharyngeal swabs and bypass RNA extraction protocols through a brief chemical and physical lysis step. Test sensitivity and specificity were assessed against gold-standard detection methods in laboratory and field conditions. For samples with Ct values below 25, direct RT-LAMP showed 83% sensitivity and 90% specificity under laboratory conditions and 91% sensitivity and 92% specificity under field conditions. The nucleocapsid antigen antibody assay had 99% positive percent agreement (PPA) and 97% negative percent agreement (NPA), outperforming spike-RBD antigen (98% PPA, 92% NPA) and seroprevalence (98% PPA, 88% NPA) under laboratory conditions. Under field conditions, similar results were found for antibody detection for the nucleocapsid antigen (93% PPA; 100% NPA), spike-RBD (100% PPA; 94% NPA), and seroprevalence (90% PPA; 94% NPA). This study validated the PLUM device as a dual POC tool for direct RT-LAMP-based SARS-CoV-2 and ELISA-based COVID-19 antibody detection.
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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 | 0.000 |
| 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".