Estimation of the under-reporting rate for the surveillance of <i>Escherichia coli</i> O157[ratio ]H7 cases in Ontario, Canada
Bibliographic record
Abstract
Two models estimating the proportion of Escherichia coli O157:H7 cases not reported in the Ontario notifiable diseases surveillance system are described. The first model is a linear series of adjustments in which the total number of reported cases is corrected by successive underreporting coefficients. The structure of the second model is based on a relative difference in the proportion of E. coli O157:H7 cases which are hospitalized between the surveillance database and the underlying population. Based on this analysis, the rate of under-reporting of symptomatic cases of E. coli O157:H7 infection in Ontario ranges from 78 to 88% corresponding to a ratio of 1 reported case for approximately 4-8 symptomatic cases missed by the surveillance system. This study highlights the need to increase awareness among public health workers of the potential biases that may exist in the interpretation of routine surveillance data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".