Extraction-free clinical detection of SARS-CoV-2 virus from saline gargle samples using Hamilton STARlet liquid handler
Bibliographic record
Abstract
As part of the COVID-19 pandemic, clinical laboratories have been faced with massive increases in testing, resulting in sample collection systems, reagent, and staff shortages. We utilized self-collected saline gargle samples to optimize high throughput SARS-CoV-2 multiplex polymerase chain reaction (PCR) testing in order to minimize cost and technologist time. This was achieved through elimination of nucleic acid extraction and automation of sample handling on a widely available robotic liquid handler, Hamilton STARlet. A customized barcode scanning script for reading the sample ID by the Hamilton STARlet's software system was developed to allow primary tube sampling. Use of pre-frozen SARS-CoV-2 assay reaction mixtures reduced assay setup time. In both validation and live testing, the assay produced no false positive or false negative results. Of the 1060 samples tested during validation, 3.6% (39/1060) of samples required retesting as they were either single gene positive, had internal control failure or liquid aspiration error. Although the overall turnaround time was only slightly faster in the automated workflow (185 min vs 200 min), there was a 76% reduction in hands-on time, potentially reducing staff fatigue and burnout. This described process from sample self-collection to automated direct PCR testing significantly reduces the total burden on healthcare systems in terms of human resources and reagent requirements.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".