Dorfman pooling enhances SARS-CoV-2 large-scale community testing efficiency
Bibliographic record
Abstract
PCR-based analysis is the gold standard for detection of SARS-CoV-2 and was used broadly throughout the pandemic. However, heightened demand for testing put strain on diagnostic resources and the adequate amount of PCR-based testing required exceeded existing testing capacity. Pooled testing strategies presented an effective method to increase testing capacity by decreasing the number of tests and resources required for laboratory PCR analysis of SARS-CoV-2. We sought to conduct an analysis of SARS-CoV-2 pooling schemes to determine the sensitivity of various sized Dorfman pooling strategies and evaluate the utility of using such pooling strategies in diagnostic laboratory settings. Overall, a trend of decreasing sensitivity with larger pool sizes was observed, with modest sensitivity losses in the largest pools tested, and high sensitivity in all other pools. Efficiency data was then calculated to determine the optimal Dorfman pool sizes based on test positivity rate. This was correlated with current presumptive test positivity to maximize the number of tests saved, thereby increasing testing capacity and resource efficiency in the community setting. Dorfman pooling methods were evaluated and found to offer a high-throughput solution to SARS-CoV-2 clinical testing that improve resource efficiency in low-resource environments.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".