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Record W2913144426 · doi:10.5740/jaoacint.18-0245

Comparison of Legiolert and a Conventional Culture Method for Detection of Legionella pneumophila from Cooling Towers in Québec

2019· article· en· W2913144426 on OpenAlexaboutno aff
Isabelle Barrette

Bibliographic record

VenueJournal of AOAC International · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsnot available
Fundersnot available
KeywordsLegionella pneumophilaLegionellaCooling towerAgarMicrobiologyLegionnaires' diseaseAgar plateEnvironmental scienceBiologyWater coolingBacteriaEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.348
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2019
Admission routes1
Has abstractyes

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