Multiple endocrine diseases in dogs: 35 cases (1996–2009)
Bibliographic record
Abstract
OBJECTIVE: To characterize a population of dogs from a tertiary care center with 2 or more endocrine disorders, including the specific disorders and time intervals between diagnosis of each disorder. DESIGN: Retrospective case series. ANIMALS: 35 dogs with 2 or more endocrine disorders. PROCEDURES: Medical records were reviewed, and the following was recorded: clinical signs, physical examination findings, and the results of CBC, serum biochemical analysis, urinalysis, aerobic bacterial culture of urine samples, endocrine testing, diagnostic imaging, and necropsy. RESULTS: 35 dogs with more than 1 endocrine disorder were identified. Seventy-seven percent (27/35) of the dogs were male, and the mean age at the time of diagnosis of the first endocrinopathy was 7.9 years. Miniature Schnauzer was the most common breed. Twenty-eight of 35 (80%) dogs had 2 disorders; 7 (20%) had 3 disorders. The most common combinations of disorders included diabetes mellitus and hyperadrenocorticism in 57.1 % (20/35) of dogs; hypoadrenocorticism and hypothyroidism in 22.9% (8/35) of dogs; and diabetes mellitus and hypothyroidism in 28.6% (10/35) of dogs. A mean of 14.5 months elapsed between diagnosis of the first and second endocrine disorders, whereas there was a mean of 31.1 months between diagnosis of the first and third endocrine disorders. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that the occurrence of multiple endocrine disorders was uncommon in dogs. The most common combinations of endocrine disorders in this population of dogs were diabetes mellitus and hyperadrenocorticism, followed by hypoadrenocorticism and hypothyroidism.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".