Estimating the incidence of subclinical infections with<i>Legionella Pneumonia</i>using data augmentation: analysis of an outbreak in The Netherlands
Bibliographic record
Abstract
Infections with Legionella bacteria can cause a potentially lethal form of pneumonia known as legionnaires' disease. In 1999 a major outbreak, causing 31 deaths, occurred among visitors and exhibitors of a consumer fair in The Netherlands. The epidemiology of subclinical infections is largely unknown, as there is no reliable method to diagnose such infections. To explore the incidence of subclinical infections, IgG and IgM antibody levels among exhibitors were compared to those among a representative sample of the Dutch population. As exhibitors were assumed to comprise both infected and uninfected individuals, their antibody levels were modelled as a mixture distribution. As infected individuals are expected to cluster around a point source, the spatial aspect of the spread of infections was taken into account. To estimate the distribution of antibody levels among infected individuals and to impute infection status among exhibitors, data augmentation was used. Subclinical infection appeared to be very common and its frequency declined with the distance from the putative source of the outbreak.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".