Incidence and risk factors of heat‐related illness in dogs from New South Wales, Australia (1997–2017)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heat Related Illness (HRI) in dogs is expected to increase as heatwaves surge due to global warming. The most severe form of HRI, heat stroke, is potentially fatal in dogs. The current study investigated the incidence and risk factors for HRI in dogs in NSW, Australia, from 1997 to 2017. We identified 119 HRI cases during this period, with a fatality rate of 23%. Dog breeds at elevated risk of HRI were Australian Stumpy Tail Cattle Dog, British Bulldog, French Bulldog, Maremma Sheepdog, Italian Greyhound, Chow Chow, Airedale Terrier, Pug, Samoyed, English Springer Spaniel, Labrador Retriever, Golden Retriever, Cavalier King Charles Spaniel, Border Collie, Staffordshire Bull Terrier, and pooled non-Australian National Kennel Council breeds (which included the American and Australian Bulldog) when compared with cross breeds (i.e., the reference variable). As expected, HRI cases were more likely in December and January, during the Australian summer and during hotter years (e.g., 2016). There were no differences in the risk of HRI between males and females nor between desexed or un-desexed dogs; but older dogs were at increased risk of HRI. These findings underscore the need for data collection that will enable the incidence of HRI in dogs to be monitored and to better understand canine risk factors particularly as temperatures will continue to rise due to global warming. The risk of mortality from HRI underpins the need for education programs focussed on prevention and early identification of HRI so that owners present affected dogs to their veterinarian as promptly as possible.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it