The Worst of Both Worlds: Examining the Hypervirulence of the Shigatoxigenic/Enteroaggregative Escherichia coli O104:H4
Bibliographic record
Abstract
(See the major article by Boisen et al on pages 1909–19.) Infectious diarrhea remains one of the most common causes of human illness. While by far the greatest burden of diarrhea is in young children in developing areas, morbidity and mortality from diarrheal pathogens can occur in anyone, anywhere, at any time. Nowhere is this broad vulnerability more evident than in large outbreaks of food-borne or waterborne diarrhea. Some of the most frightening of these outbreaks have been due to enterohemorrhagic Escherichia coli (EHEC), including the notorious O157:H7 serotype, which causes hemorrhagic colitis and its potentially lethal complication, hemolytic-uremic syndrome (HUS). In 2011, a large, unusual, and devastating outbreak of hemorrhagic colitis due to E. coli occurred in northern Europe, affecting around 4000 people [1]. The responsible organism was not O157:H7 but rather belonged to a rarely reported serotype, O104:H4. The origin of the outbreak was ultimately traced to fenugreek sprouts imported from African seeds and produced at a farm in Germany, and the majority of cases and deaths occurred in that country. What was particularly worrisome in this outbreak was the unusually high rate of HUS, estimated at 22% and resulting in 54 fatalities. In contrast, historical HUS rates with O157:H7 are estimated at 5%–10%.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".