Comparison of Legiolert and a Conventional Culture Method for Detection of Legionella pneumophila from Cooling Towers in Québec
Bibliographic record
Abstract
Abstract Background: Legionnaires’ disease is a potentially lethal pneumonia contracted through inhalation of aerosolized water contaminated with Legionella bacteria. Detection and control of L. pneumophila, the primary species responsible for the disease, is critical to public health. In Québec, cooling towers and evaporative condensers are required to follow a maintenance and testing program to ensure L. pneumophila concentrations remain at acceptable levels. Objective: This study compared a new culture method based on the most probable number approach, Legiolert®, with the formal culture method used at EnvironeX for regulatory compliance testing to quantify L. pneumophila from cooling tower waters in Québec. Methods: A split-sample analysis was performed in which 401 samples from cooling towers in Québec were tested with both methods. Results: Results with 74 positive samples showed that Legiolert provided a significant increase in sensitivity for L. pneumophila compared with the agar plate method. Cooling tower samples often contain non-Legionella flora that necessitate multiple treatment and plating conditions to prevent interference with the test. Legiolert showed little to no impact from non-Legionella organisms in this study. Conclusions: Overall, Legiolert showed several advantages over the agar plate method, including increased sensitivity, reduced interference, a simplified test procedure, and an easy-to-read positive signal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".