Pathophysiology of heatstroke in dogs – revisited
Bibliographic record
Abstract
Heatstroke results from a failure to dissipate accumulated heat during exposure to hot environments, or during strenuous physical exercise under heat stress. It is characterized by core body temperatures > 41°C, with central nervous system dysfunction. Functional morphology and thermoregulatory effectors differences between dogs and humans may require special heatstroke protective adaptations in dogs, however, the risk factors for developing heatstroke are similar in both. In dogs, these include hot, especially highly humid environments, excessive physical activity, obesity, large (>15 kg) body weight, being of certain breed (e.g., Labrador retrievers and brachycephalic breeds), upper airway obstruction and prolonged seizures. Lack of acclimation to heat and physical fitness decreases the survival of heat stroked dogs. At the systemic level, blood pooling within the large internal organs (e.g., spleen, liver) is a major contributor to the development of shock and consequent intestinal ischemia, hypoxia and endothelial hyperpermeability, commonly occurring in heatstroke patients. Evoked serious complications include rhabdomyolysis, acute kidney injury, acute respiratory distress syndrome and ultimately, sepsis and disseminated intravascular coagulation. The most common clinical signs in dogs include acute collapse, tachypnea, spontaneous bleeding, shock signs and mental abnormalities, including depression, disorientation or delirium, seizures, stupor and coma. In such dogs, presence of peripheral blood nucleated red blood cells uniquely occurs, and is a highly sensitive diagnostic and prognostic biomarker. Despite early, appropriate body cooling, and intensive supportive treatment, with no available specific treatment to ameliorate the severe inflammatory and hemostatic derangements, the mortality rate is around 50%, similar to that of human heatstroke victims. This review discusses the pathophysiology of canine heatstroke from a veterinarian's point of view, integrating new and old studies and knowledge.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".