Real-time syndrome surveillance in Ontario, Canada: the potential use of emergency departments and Telehealth
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to review new bioterrorist and emerging infectious threats to public health in Ontario, Canada, and to propose a means of integrating a telephone-based health information service and emergency department triage with a first-line real-time, 24-h a day syndrome surveillance system. This automated system could be beneficial in detecting a bioterrorist threat as well as in detecting and monitoring disease outbreaks such as influenza, Norwalk, West Nile virus, Escherichia coli 0157 or severe acute respiratory syndrome. METHOD: The Medline PubMed database was searched for articles relating to bioterrorism and syndromic surveillance from 1997 onwards. The websites of the Ontario Ministry of Health and Long-Term Care, Ontario Ministry of Public Safety and Security, Centers for Disease Control and Canadian Population and Public Health Branch of Health Canada were searched for articles relating to bioterrorism and syndromic surveillance. Interviews were conducted with key informants from Telehealth staff, the public health services of Ontario and the Centers for Disease Control and Prevention, Atlanta, GA, USA. RESULTS: Real-time syndrome surveillance is a new means of detecting disease outbreaks or possibly acts of bioterrorism at the first contact with the healthcare system. It has been used successfully to detect influenza outbreaks at an early stage. The system that is proposed would be a province-wide integrated early warning system for both bioterrorist events and emerging infections. It would use clusters of symptoms tied to temporal, demographic and spatial data to increase sensitivity and specificity. CONCLUSION: Real-time syndrome surveillance is an evolving science. Emergency departments and Telehealth in Ontario lend themselves as first contacts to the healthcare system as excellent opportunities to perform syndrome surveillance. They offer the opportunity properly to identify at-risk patients for emerging infections by including contact and travel data into the symptom complex. This could identify at-risk patients early and lead to appropriate public health measures. The benefit of using Telehealth in Ontario is the provincial accessibility of Telehealth and the extensive data collected on one computerized system. Emergency departments should also have a uniform computerized triage data collection system to facilitate surveillance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".