Dispatches The Impact of Health Communication and Enhanced Laboratory-Based Surveillance on Detection of Cyclosporiasis Outbreaks in California
Bibliographic record
Abstract
We investigated the timing of diagnosis, influence of media information on testing for Cyclospora, and the method used to identify cases during eight cyclosporiasis outbreaks in California in spring of 1997. We found that Internet information, media reports, and enhanced laboratory surveillance improved detection of these outbreaks. Cyclospora cayetanensis, a recently identified coccidian parasite of humans (1), causes explosive watery diarrhea that can persist for several weeks (2). The oocysts cannot be detected by standard ova and parasite testing; methods specific for Cyclospora include modified acid-fast or other stains, autofluorescence with ultraviolet epifluorescence microscopy, and wet mount under phase-contrast microscopy (3). The infection can be treated with trimethoprimsulfamethoxazole (TMP-SMX) (4). Eight outbreaks in California were among the 41 eventassociated clusters of cyclosporiasis associated with Guatemalan raspberries identified in the United States and Canada in 1997 (5). We examined factors contributing to the identification of outbreaks of this emerging infectious disease. The detection of outbreaks was enhanced by Internet information that patients brought to their physicians, media reports, and enhanced laboratory-based (ELB) surveillance. ELB surveillance, part of the
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".