The Adequacy of Current Legionnaires’ Disease Diagnostic Practices in Capturing the Epidemiology of Clinically Relevant Legionella: A Scoping Review
Bibliographic record
Abstract
Legionella is an underdiagnosed and underreported etiology of pneumonia. Legionella pneumophila serogroup 1 (LpSG1) is thought to be the most common pathogenic subgroup. This assumption is based on the frequent use of a urinary antigen test (UAT), only capable of diagnosing LpSG1. We aimed to explore the frequency of Legionella infections in individuals diagnosed with pneumonia and the performance of diagnostic methods for detecting Legionella infections. We conducted a scoping review to answer the following questions: (1) “Does nucleic acid testing (NAT) increase the detection of non-pneumophila serogroup 1 Legionella compared to non-NAT?”; and (2) “Does being immunocompromised increase the frequency of pneumonia caused by non-pneumophila serogroup 1 Legionella compared to non-immunocompromised individuals with Legionnaires’ disease (LD)?”. Articles reporting various diagnostic methods (both NAT and non-NAT) for pneumonia were extracted from several databases. Of the 3449 articles obtained, 31 were included in our review. The most common species were found to be L. pneumophila, L. longbeachae, and unidentified Legionella species appearing in 1.4%, 0.9%, and 0.6% of pneumonia cases. Nearly 50% of cases were caused by unspecified species or serogroups not detected by the standard UAT. NAT-based techniques were more likely to detect Legionella than non-NAT-based techniques. The identification and detection of Legionella and serogroups other than serogroup 1 is hampered by a lack of application of broader pan-Legionella or pan-serogroup diagnostics.
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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 | 0.023 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".