Look before diving into pooling of SARS-CoV-2 samples on high throughput analyzers
Bibliographic record
Abstract
Abstract Given the unprecedented demand for SARS-CoV-2 testing during the COVID-19 pandemic, the benefits of specimen pooling have recently been explored. As previous studies were limited to mathematical modeling or testing on low throughput PCR instruments, this study aimed to assess pooling on high throughput analyzers. To assess the impact of pooling, SARS-CoV-2 dilutions were performed at varying pool depths (i.e. 1:2, 1:4, and 1:8) into test-negative nasopharyngeal or oropharynx/anterior nares swabs matrix. Testing was evaluated on the automated Roche Cobas 6800 system, or the Roche MagNApure LC 2.0 or MagNAPure 96 instruments paired with a laboratory-developed test using a 96-well PCR format. The frequency of detection in specimens with low viral loads was evaluated using archived specimens collected throughout the first pandemic wave. The proportion of detectable results per pool depths was used to estimate the potential impact. In addition, workflow at the analytical stage, and pre-and post-stages of testing were also considered. The current study estimated that pool depths of 1:2, 1:4, and 1:8 would have allowed the detection of 98.3%, 96.0%, and 92.6% of positive SARS-CoV-2 results identified in the first wave of the pandemic in Nova Scotia. Overall, this study demonstrated that pooling on high throughput instrumentation can dramatically increase the overall testing capacity to meet increased demands, with little compromising to sensitivity at low pool depths. However, the human resources required at the pre-analytical stage of testing is a particular challenging to achieve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".