Stool submission data to help inform population-level incidence rates of enteric disease in a Canadian community
Bibliographic record
Abstract
Laboratory-based surveillance data is essential for monitoring trends in the incidence of enteric disease. Current Canadian human enteric surveillance systems report only confirmed cases of human enteric disease and are often unable to capture the number of negative test results. Data from 9116 hospital stool specimens from the Waterloo Region in Canada, with a mixed urban and rural population of about 500 000 were analysed to investigate the use of stool submission data and its role in reporting bias when determining the incidence of enteric disease. The proportion of stool specimens positive for Campylobacter spp. was highest in the 15-29 years age group, and in the 5-14 years age group for Salmonella spp. and E. coli O157:H7. By contrast, the age-specific incidence rates were highest for all three pathogens in the 0-4 years age group which also had the highest stool submission rate. This suggests that variations in age-specific stool submission rates are influencing current interpretation of surveillance data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".