The Big Pet Diabetes Survey: Perceived Frequency and Triggers for Euthanasia
Bibliographic record
Abstract
Current pet diabetes mellitus (DM) treatment necessitates the active daily involvement of owners and can be costly. The current study aimed to investigate the owner population which opts for euthanasia instead of DM treatment. A survey was designed using multiple feedback steps and made available online to veterinarians world-wide. A total of 1192 veterinarians completed the survey and suggested a median one in 10 diabetic pets are euthanased at diagnosis; a further median one in 10 within one year because of lack of success or compliance. Perceived most important motivating factors included "presence concurrent disease" (45% respondents); "costs" (44%); "animal age" (37%); "problems obtaining adequate control" (35%); "pet welfare" (35%); and "impact owner's lifestyle" (32%). Cats in Canadian (odds ratio (OR) 2.7), Australian (OR 2.3), rural (OR 1.6) and mixed (OR 1.7) practices were more likely to be euthanased because of DM diagnosis, while cats presented to referral/university were less likely to be euthanased (OR 0.6). Dogs were more likely to be euthanased because of DM in Canadian (OR 1.8), rural (OR 1.8) and mixed (OR 1.6) practices. The survey results suggest that benefit exists in improved DM education with emphasis on offering a choice of treatment styles ranging from intense and expensive to hands-off and cheap.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".