Patricia M. Griffin, Section Editor Estimating the Number of Human Cases of
Bibliographic record
Abstract
A stochastic model was used to estimate the number of human cases of ceftiofur-resistant Salmonella enterica serovar Heidelberg in Québec and Ontario attributable to chicken consumption and excess cases attributable to human prior antimicrobial consumption. The annual mean incidence of S. Heidelberg (Québec/Ontario) de-creased from 70/62 cases per 100 000 in 2004 to 29/30 cases per 100 000 in 2007 (Québec)/2008 (Ontario), in-creasing to 59/45 cases per 100 000 in 2011. The annual mean incidence of ceftiofur-resistant cases from chicken decreased from 8/7 cases per 100 000 in 2004 to 1/1 cases per 100 000 in 2007 (Québec)/2008 (Ontario), increas-ing to 7/5 cases per 100 000 in 2011. The annual mean total number of excess ceftiofur-resistant cases from chicken attributable to human prior antimicrobial consumption (Québec/Ontario) decreased from 71/123 in 2004 to 6/24 in 2007 (Québec)/2008 (Ontario), but increased to 62/91 in 2011. This model will support future work to determine the increased severity, mortality and healthcare costs for ceftiofur-resistant Salmonella Heidelberg infections. These results provide a basis for the evaluation of future public health interventions to address antimicrobial resistance.
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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 | 0.003 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".