Analysis of Outbreak Data Reveals Factors Contributing to Salmonellosis Outbreaks Linked to Cantaloupes
Bibliographic record
Abstract
Over the last thirty years, the presence of Salmonella spp. on cantaloupes has been linked to multiple large and deadly foodborne outbreaks in multiple countries. To identify factors associated with the number of cases and risk of severe illness in these outbreaks, information from previous melon-associated salmonellosis outbreaks was analyzed. Data were collected from sixty outbreak investigations. Compared to other melon types, such as watermelon, honeydew, and Galia melon, cantaloupes had the highest public health burden. Cantaloupes were implicated in 43% of reported melon-related outbreaks, 51% of melon-related laboratory-confirmed cases, 54% of melon-related hospitalizations, and 76% of melon-related deaths. In the United States, imported cantaloupes were associated with higher rates of severe salmonellosis and a greater diversity of Salmonella spp. serovars compared to domestically grown cantaloupes. Cantaloupes implicated in outbreaks were equally likely to have been consumed in either private or public settings. Larger outbreaks were associated with the consumption of precut cantaloupe and/or the consumption of cantaloupes in public settings. With the identification of these contributing factors, a literature search was conducted to assess the state of knowledge concerning Salmonella and cantaloupes. Several gaps in the literature were noted and are discussed in the context of reducing the number of illnesses associated with the presence of Salmonella on cantaloupes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".