Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience
Bibliographic record
Abstract
Innovation in laboratory testing algorithms to address seemingly uncontrollable global supply chain shortages in plastics and other consumables during emergencies such as the current COVID-19 pandemic have been urgently needed. We report our experience with specimen pooling on SARS-CoV-2 testing in an acute care hospital microbiology laboratory during a high testing demand period that exceeded available processing capacity. A fully automated four-in-one pooling algorithm was designed and validated. Correlation and agreement were calculated. A custom Microsoft Excel tool was designed for use by the technologists to aid interpretation, verification and result entry. Cost-per-test impact for pooling was measured in reference to the consumable cost and was denoted as the percentage reduction of cost versus the baseline cost-per-test of testing specimens individually. Validation showed a strong correlation between the signals observed when testing specimens individually versus those that were pooled. Average crossing point difference was 1.352 cycles (95% confidence interval of -0.235 and 2.940). Overall agreement observed between individually and pooled tested specimens was 96.8%. Stratified agreement showed an expected decreased performance of pooling for weakly positive specimens dropping below 60% after a crossing point of 35. Post-implementation data showed the consumable cost-savings achieved through this algorithm was 85.5% after 8 months, creating both testing and resource capacity. Pooling is an effective method to be used for SARS-CoV-2 testing during the current pandemic to address resource shortages and provide quick turnaround times for high test volumes without compromising performance.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".