The Canadian contribution to the science of verotoxigenic<i>Escherichia coli</i>and associated illnesses: the early years
Bibliographic record
Abstract
As stories of microbiological and infectious disease discoveries are told, one of the most charming of these in Canadian history is the recognition of verotoxigenic Escherichia coli (VTEC) and associated disease. The considerable burden and impact of VTEC-associated infections is currently experienced worldwide. Jack Konowalchuk, Joan Speirs, and their collaborators in Ottawa, Ontario, defined the E. coli verotoxin. Mohamed Karmali, Martin Petric, and colleagues at The Hospital for Sick Children in Toronto, Ontario, established the association of VTEC and hemolytic-uremic syndrome. Nationwide, and with the dissemination of knowledge through the central health directorate in Ottawa, numerous scientists and clinicians were motivated to focus on this theme, and within a relatively brief chronology, much became known about the biology of VTEC and the pathogenesis, epidemiology, and clinical aspects of disease. Many Canadian investigators, but especially those in the veterinary school at Guelph, Ontario, also contributed to the science of VTEC among animals. The interactions between clinical and veterinary researchers led to a then unprecedented exponential growth in the knowledge base of VTEC. Canadians also participated in a better understanding of the origin and potential of the E. coli O157 serogroup. Whereas not exclusively Canadian, the contributions of our national scientists in this field must be seen as a vital part of medical and microbiological Canadiana; this essence is captured in this historical review.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".