Serum cholesterol disturbances in dogs with common endocrinopathies at the time of diagnosis: a retrospective study
Bibliographic record
Abstract
Abstract Background Although dyslipidemia is commonly reported in dogs, comparative data on the magnitude of serum cholesterol disturbances have not been reported. We aimed to describe the severity of hyper- and hypocholesterolemia in dogs with common endocrinopathies and to evaluate its association with common laboratory parameters. Medical records were reviewed over a decade (2011–2022) for dogs with hypothyroidism, diabetes mellitus (DM), hyperadrenocorticism (HAC), or hypoadrenocorticism (HA), and included signalment, common laboratory and diagnostic imaging parameters, comorbidities, and medications. This retrospective study included 53 dogs with hypothyroidism, 54 with DM, 62 with HAC, and 79 with HA. Results Medians [range] of serum cholesterol concentration ([Chol]s) for dogs with hypothyroidism, DM, HAC, and HA were 492 [174–1829], 321 [116–928], 309 [151–630], and 112 mg/dL [31–309], and hypercholesterolemia was reported in 91%, 85%, 81%, and 9% for each disorder, respectively. Median [Chol]s was significantly higher in hypothyroid dogs with a serum thyroxine concentration < 0.47 (A = 607) vs. ≥0.47 ug/dL (B = 324 mg/dL) (B-A = -299 mg/dL; 95.21% CI of difference = [-433; -166]; p < .0001), and significantly lower in HAC dogs with serum ALP activity < 1,000 U/L (A = 275) vs. ≥1,000 (B = 360 mg/dL) (B-A = + 74 mg/dL; 95.14% CI of difference = [+ 25; +121], p = .006). Comparison among all studied endocrinopathies showed that median [Chol]s was significantly higher in hypothyroid dogs and significantly lower in HA dogs, whereas median [Chol]s was similar in HAC and DM dogs. Conclusions Serum cholesterol concentration can serve as a valuable tool to suspect certain canine endocrinopathies.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".