Heat Stroke in Dogs: a Retrospective Study of 54 Cases (1999–2004) and Analysis of Risk Factors for Death
Bibliographic record
Abstract
The medical records of 54 dogs presented to the Hebrew University Veterinary Teaching Hospital and diagnosed with heat stroke were retrospectively reviewed. Data abstracted included history, clinical and clinicopathological signs at admission, treatment, disease progression, and outcome. Exertional and environmental heat stroke were present in 63% (34 of 54) and 37% (20 of 54) of the dogs, respectively, and 78% (42 of 54) were examined between June and August. The mean temperature and heat discomfort index in the particular days of heat stroke were significantly increased (P < .001, P < .001, respectively) compared with their corresponding average daily values. In 27 dogs the body temperature was > or = 41 degrees C (105.8 degrees F). Belgian Malinois (15%, odds ratio [OR] = 24, 95% confidence interval [CI95%] 8.2-64.5), Golden and Labrador Retrievers (21%, OR = 2.08, CI95% 0.95-4.2), and brachycephalic breeds (25%, OR = 1.7, CI95%], 0.81-3.21) were overrepresented, whereas small breeds (<8 kg) were underrepresented (2%, OR = 0.08, CI95%, 0.002-0.48). Thrombocytopenia (45 of 54 dogs) and prolongation of the prothrombin (PT) and activated thromboplastin (aPTT) times (27 of 47 dogs) were recorded during hospitalization. Disseminated intravascular coagulation (P = .013) and acute renal failure (P = .008), diagnosed in 28 of 54 and 18 of 54 of the cases, respectively, were risk factors for death. The overall mortality rate was 50%. Hypoglycemia (<47 mg/dL, P = .003), prolonged PT (>18 seconds, P = .05), and aPTT (>30 sec, P < .001) at admission were associated with death. Serum creatinine >1.5 mg/dL (P = .003) after 24 hours, delayed admission (>90 minutes, P = .032), seizures (P = .02), and obesity (P = .04) were also risk factors for death. Heat stroke in dogs results in serious complications and high fatality rate despite appropriate treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".