Source attribution of human campylobacteriosis at the point of exposure by combining comparative exposure assessment and subtype comparison based on comparative genomic fingerprinting
Bibliographic record
Abstract
Human campylobacteriosis is a common zoonosis with a significant burden in many countries. Its prevention is difficult because humans can be exposed to Campylobacter through various exposures: foodborne, waterborne or by contact with animals. This study aimed at attributing campylobacteriosis to sources at the point of exposure. It combined comparative exposure assessment and microbial subtype comparison with subtypes defined by comparative genomic fingerprinting (CGF). It used isolates from clinical cases and from eight potential exposure sources (chicken, cattle and pig manure, retail chicken, beef, pork and turkey meat, and surface water) collected within a single sentinel site of an integrated surveillance system for enteric pathogens in Canada. Overall, 1518 non-human isolates and 250 isolates from domestically-acquired human cases were subtyped and their subtype profiles analyzed for source attribution using two attribution models modified to include exposure. Exposure values were obtained from a concurrent comparative exposure assessment study undertaken in the same area. Based on CGF profiles, attribution was possible for 198 (79%) human cases. Both models provide comparable figures: chicken meat was the most important source (65-69% of attributable cases) whereas exposure to cattle (manure) ranked second (14-19% of attributable cases), the other sources being minor (including beef meat). In comparison with other attributions conducted at the point of production, the study highlights the fact that Campylobacter transmission from cattle to humans is rarely meat borne, calling for a closer look at local transmission from cattle to prevent campylobacteriosis, in addition to increasing safety along the chicken supply chain.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".