Legionella Test Reporting at a Regional Veteran Affairs Medical Center
Bibliographic record
Abstract
Abstract Introduction/Objective The appropriate reporting and monitoring of legionella antigen and culture test volumes and results is a veteran affairs quality assurance regulatory requirement. Legionnaires disease incidence has been noted to be increasing in the United States, and it has also been documented that health care facilities are especially high- risk settings for the transmission of Legionella bacteria from the building water systems to the occupants. However, studies of Legionella test epidemiology for non-veteran hospitals is sparse in the literature. To provide perspective, here we report the total legionella antigen and culture tests from quarter 1 2016 to quarter 4 2019 to provide a regional perspective. Methods Quality assurance data on both the total number of Legionella antigen and culture tests and the recorded number of positive cases were reviewed from quarter 1 2016 to quarter 4 2019 and tabulated. This data is collected routinely as per veteran affairs regulatory directives. Results There were a total of 1613 legionella antigen tests and 1287 legionella cultures. None of the Legionella cultures were positive during the study period. Nonetheless, there were 3 positive urinary antigen tests for a total calculated percentage of 0.00002%. Conclusion The presence of positive Legionella antigen tests at a regional veteran affairs medical center indicates that a robust quality assurance program is tremendous benefit to monitoring Legionella at a major medical institution in order to prompt action to prevent hospital-based spread.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".