A common-source outbreak of trichinosis from consumption of bear meat.
Bibliographic record
Abstract
This paper discusses an outbreak of trichinosis that occurred in 1998 in Montgomery County, Ohio, and the investigation that followed. The outbreak was associated with consumption of bear meat from a hunt in Ontario, Canada. The person who had the index case had eaten two bear burgers that were cooked rare in a microwave oven. Bear meat from the same hunt later was consumed by 15 other people at a church supper and an additional 13 people who did not attend the supper. Of the 15 attendees at the church supper who ate the bear meat, seven developed illness consistent with Trichinella infection (attack rate about 47 percent). An additional seven people attended the supper but did not eat the bear meat and did not become ill. Having eaten bear meat at the church supper was associated with an increased risk of illness (p = .05). Inadequate cooking of the bear meat resulted in the transmission of live trichinae. The 13 other people who ate the bear meat but did not attend the supper reported no illness. A total of eight people, including the person with the index case, met the case definition for trichinosis. Adequate cooking of the bear meat or consumption of uninfected portions of the meat was probably the protective factor for those who did not become ill after consuming the bear meat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".