Incidence and risk factors of heat‐related illness in dogs from New South Wales, Australia (1997–2017)
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".