Performance evaluation of a low-throughput qPCR-based <i>Legionella</i> assay for utility as an onsite industrial water system monitoring method
Bibliographic record
Abstract
Legionella is a bacterial genus found in natural aquatic environments, as well as domestic and industrial water systems. Legionella presents potential human health risks when aerosolized and inhaled by at-risk individuals and is commonly monitored at locations with likelihood of proliferation and human exposure. Legionella monitoring is widely performed using culture-based testing, which faces limitations including turnaround time and interferences. Molecular biology methodologies, including quantitative polymerase chain reaction (qPCR), are being explored to supplement or replace culture-based testing because of faster turnaround and lower detection limits, allowing for more rapid water remediation measures. In this study, three methods were compared by testing industrial water samples: culture-based testing by a certified lab, high throughput qPCR testing (HT qPCR), and field deployable low throughput qPCR testing (LT qPCR). The qPCR test methods reported more positive results than culture testing, indicating improved sensitivity and specificity. The LT qPCR test is portable with quick turnaround times, and can be leveraged for environmental surveillance, process optimization, monitoring, and onsite case investigations. The LT qPCR test had high negative predictive value and would be a useful tool for negative screening of Legionella samples from high-risk environments and/or outbreak investigations to streamline samples for culture testing. ONE-SENTENCE SUMMARY: This study compared three test methods for Legionella to evaluate performance of a low throughput quantitative polymerase chain reaction (LT qPCR) test for Legionella that can be used onsite; the study found that the high throughput (HT) and LT qPCR tests used in this study gave more positive results than culture testing, and the results indicated a similar negative predictive value for the HT and LT qPCR tests, supporting that the LT qPCR method could be useful for negative screening of Legionella samples in industrial water systems onsite.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".