Determining The Heat and Cold-Related Excess Morbidity in the Golden Horseshoe Region of Ontario
Bibliographic record
Abstract
The objective of this study is to quantify the excess morbidity related to extreme heat and cold events in Ontario’s healthcare system. The aim is to develop a greater understanding of how the “ENVIRO” syndrome and other ailments that are monitored for aberrational activity within Acute Care Enhanced Surveillance (ACES) are influenced by extreme heat and cold events. This may allow for conclusions to be drawn regarding the excess population morbidity related to these events in Ontario. \nThe specific research questions that will be addressed in this study are as follows: \n1)\tDuring a heat or cold event, which of the 15 syndromes monitored for aberrational activity are elevated in addition to the established “ENVIRO” syndrome? \n2)\tTo give a proxy of excess morbidity across all health outcomes during a heat or cold event, what is the difference between the overall Emergency Department (ED) visit volume during a heat or cold event and the non-event baseline visit volume? \n \nThis study aims to determine the relationship between extreme heat and cold events and visits to the ED, ultimately contributing to preparatory methods for these events and a reduction in morbidity. By determining the excess morbidity related to extreme heat and cold events, a more holistic approach to preparation and prevention is feasible within the Golden Horseshoe region of Ontario. As the climate changes, these studies must be conducted to determine the excess morbidity that the healthcare system will face due to extreme weather events as they become more frequent and unpredictable. Syndromic surveillance systems can be developed and improved based on the scientific findings of this study, enabling a more prepared public health response (CEC, 2017).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".