Lessons Learned from Pseudo-Oubreak of Legionella pneumophilia Serogroup 1 Infections
Bibliographic record
Abstract
Legionnaires disease (LD) has been increasingly identified in communities, as well as healthcare-associated outbreaks linked to contaminated water in hospitals. Since Legionella can be found in man-made water systems, hospitals are particularly vigilant to ensure patient safety and minimize risk for Legionella exposure. We report the investigation of marked increase in number of pneumonia cases due to Legionella pneumophila serogroup 1 over 12 month period concerning for community and possible healthcare-associated outbreak. We conducted a record review of each positive Legionella Urinary Antigen Enzyme Immunoassay (EIA) test to see if: a) case definition was met, b) risk factor for LD, c) identify common points of potential exposure, d) healthcare exposure in the last 14 days. Assessment of patient and risk for nosocomial transmission were discussed with local and state health departments. Discordance between test result and patient clinical picture led us to repeat testing on 10 patients and discuss with laboratory to reassess testing process. If case suggested nosocomial transmission, environmental and water sampling for Legionella species were collected. We reviewed calculated ratio results from plate readers. The percent of positive urine Legionella tests in 2015 was 0.57% and increased to 1.36% in 2016. This rate further increased to 6.48% in last quarter of 2016 after testing switched to a different lab with same EIA. Case review suggested a discordance of positive test result and clinical presentation starting in September. Of a total of 61 cases, 33 occurred late in the year over 10 week period. Mean age of patients was 68.5 years old. Twenty-one cases did not fit case definition and 11 patients were possible LD; these cases had “low positive” results on plate reader. The lab reviewed technique, plate washing, and plate reader calculations for the assay. Nine specimens were concurrently tested by a different lab with negative results, thus pointing to lab inconsistency. Several false positives were noted in patients that did not fit the clinical picture of LD with a “low positive” test result by ratio calculation. Due to this lab error and concern for missing a diagnosis, roughly 20 patients were unnecessarily prescribed antibiotics. All authors: No reported disclosures.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".