Evaluation and Comparison of Multiple Test Methods, Including Real-time PCR, for Legionella Detection in Clinical Specimens
Bibliographic record
Abstract
Legionella is a Gram-negative bacterium that can cause Pontiac fever, a mild upper respiratory infection and Legionnaire's disease, a more severe illness. We aimed to compare the performance of urine antigen, culture, and polymerase chain reaction (PCR) test methods and to determine if sputum is an acceptable alternative to the use of more invasive bronchoalveolar lavage (BAL). Data for this study included specimens tested for Legionella at Public Health Ontario Laboratories from 1st January, 2010 to 30th April, 2014, as part of routine clinical testing. We found sensitivity of urinary antigen test (UAT) compared to culture to be 87%, specificity 94.7%, positive predictive value (PPV) 63.8%, and negative predictive value (NPV) 98.5%. Sensitivity of UAT compared to PCR was 74.7%, specificity 98.3%, PPV 77.7%, and NPV 98.1%. Out of 146 patients who had a Legionella-positive result by PCR, only 66 (45.2%) also had a positive result by culture. Sensitivity for culture was the same using either sputum or BAL (13.6%); sensitivity for PCR was 10.3% for sputum and 12.8% for BAL. Both sputum and BAL yield similar results regardless testing methods (Fisher Exact p-values = 1.0, for each test). In summary, all test methods have inherent weaknesses in identifying Legionella; therefore, more than one testing method should be used. Obtaining a single specimen type from patients with pneumonia limits the ability to diagnose Legionella, particularly when urine is the specimen type submitted. Given ease of collection and similar sensitivity to BAL, clinicians are encouraged to submit sputum in addition to urine when BAL submission is not practical from patients being tested for Legionella.
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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 | 0.046 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".