Bibliographic record
Abstract
In June 2021, western Canada experienced an unprecedented heat wave, breaking dozens of temperature records. As a result, the region had a significant uptick in sudden deaths, emergency department visits, and hospital admissions. Under thermal stress, the human body achieves heat dissipation through evaporation of sweat and increased cutaneous blood flow. When these mechanisms are overwhelmed, the core body temperature rises, which leads to heat stroke, a life-threatening syndrome of hyperthermia and central nervous system dysfunction in the setting of an environmental thermal load. Heat dissipation relies on an intact cardiovascular system to dilate cutaneous vasculature and increase cardiac output. Individuals with impaired cardiovascular function have a limited ability to increase stroke volume, cardiac output, and blood flow to the skin, increasing the risk of heat stroke. In turn, these patients, whose cardiac condition is already compromised, are susceptible to cardiovascular complications of heat stroke, including arrhythmias, myocardial ischemia, heart failure, shock, and sudden death. Indeed, the majority of excess deaths during heat waves are cardiovascular in origin, highlighting the impact the cardiovascular system has on the development of heat stroke, and vice versa. This review summarizes the current understanding of the interaction between the cardiovascular system and heat stroke, including the pathophysiology, cardiovascular complications, and treatment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".