Characterizing the Effects of Extreme Cold Using Real-time Syndromic Surveillance, Ontario, Canada, 2010-2016
Bibliographic record
Abstract
Morbidity and mortality from exposure to extreme cold highlight the need for meaningful temperature thresholds to activate public health alerts. We analyzed emergency department (ED) records for cold temperature-related visits collected by the Acute Care Enhanced Surveillance system-a syndromic surveillance system that captures data on ED visits from hospitals in Ontario-for geographic trends related to ambient winter temperature. We used 3 Early Aberration Reporting System algorithms of increasing sensitivity-C1, C2, and C3-to determine the temperature at which anomalous counts of cold temperature-related ED visits occurred in northern and southern Ontario from 2010 to 2016. The C2 algorithm was the most sensitive detection method. Results showed lower threshold temperatures for Acute Care Enhanced Surveillance alerts in northern Ontario than in southern Ontario. Public health alerts for cold temperature warnings that are based on cold temperature-related ED visit counts and ambient temperature may improve the accuracy of public warnings about cold temperature risks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".