Bibliographic record
Abstract
Background: Extreme climates (i.e. cold, heat, rain/snow) can increase the risk of cardiovascular disease (CVD). Climate conditions on land are largely driven by Pacific [El Niño Southern Oscillation (ENSO)] and Atlantic [Atlantic Multi-decanal Oscillation (AMO)] sea surface temperature variability, which have differing regional impacts. Associations between these climate patterns and its extremes on CVD remain understudied. Objectives: To examine whether: 1) Large-scale climate patterns (ENSO/AMO) are associated with CVD event rates in older adults across North America and UK (Study 1). 2) Regional precipitation is a mediator of such associations (Study 2). 3) Precipitation type (snow/rainfall) is associated with risk of AMI or stroke admissions in the Greater Toronto Area (GTA) (Study 3). Hypotheses: ENSO/AMO, warm, and wet conditions will be significantly associated with AMI or stroke event rates among older adults in North America and UK. Methods & Design: Large population datasets (2000-2019) were used to test each objective, including the Canadian Chronic Disease Surveillance System, US CDC Wide-ranging ONline Data for Epidemiologic Research and National Statistics UK (Studies 1 and 2). Furthermore, administrative databases (Study 3). Primary outcomes were AMI/stroke admissions based on relevant ICD-10/ICD-10CA codes. Monthly or daily region-specific exposure data for sea and land surface temperature/ precipitation were ascertained from satellite data (Studies 1 and 2) and Environment Canada (Study 3). Overall Results: Study 1 showed that extreme summer ENSO and AMO were significantly associated with increases in annual AMI events in regions of Canada, the US and UK that experienced warm and wet conditions. Findings from Study 2 suggested that associations between monthly ENSO and AMI in the southwestern US were partially mediated by precipitation. Study 3 found that heavy snowfall is associated with a greater number of daily AMI admissions among older adults in the GTA; while extreme rain is associated with an elevated number of stroke admissions. Temperature was found to be a significant effect modifier. Conclusions & Relevance:This research demonstrates significant associations between climate variability and CVD outcomes. More extreme conditions are expected worldwide because of ongoing climate change, which may have a substantial impact on CVD risk, particularly in older adults.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".