Burden of acute gastrointestinal illness in Canada, 1999-2007: interim summary of NSAGI activities.
Bibliographic record
Abstract
INTRODUCTION: The National Studies on Acute Gastrointestinal Illness (NSAGI) initiative was designed to generate baseline period prevalence rates of self-reported AGI in communities across Canada, assess the burden associated with AGI, and quantify the under-reporting of AGI in Canada's national enteric disease reporting systems. METHODS: Methods utilized included population surveys administered randomly via telephone services. Three population surveys in three locations within Canada included over 10,000 residents. Questions pertained to recent symptoms as well as socio-demographic factors, use of the health care system and missed work or school due to illness. RESULTS: In summary of published results, there are an estimated 1.3 episodes of AGI per person-year and an estimated 10-47, 13-37 and 23-49 cases in the community for every case of verotoxigenic Escherichia coli, Salmonella and Campylobacter, respectively, captured within the national surveillance system. AGI represents an annual per capita cost of $115 CAD. DISCUSSION: The work of NSAGI highlights the significant burden and impact of AGI in the Canadian population. These results will also be incorporated into the current work at the World Health Organization (WHO) to estimate the global burden of food related illnesses.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| 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) | 0.003 | 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".