Neurologic Dysfunction in Hypothyroid, Hyperlipidemic Labrador Retrievers
Bibliographic record
Abstract
Abstract Background: Hypothyroidism has been associated with a variety of neurologic signs, but the mechanism for this association is not completely understood. Hypothyroidism also is associated with hyperlipidemia that predisposes to atherosclerosis, increased blood viscosity, and thromboembolic events. Objective: The objective is to characterize neurologic signs potentially associated with hyperlipidemia and atherosclerosis in canine hypothyroidism. Animals: This study used dogs referred to North Carolina State University Veterinary Teaching Hospital for evaluation of neurologic signs. Materials and Methods: A retrospective study was conducted in which medical records of dogs with neurologic signs and a diagnosis of hypothyroidism and hyperlipidemia were reviewed. Details of the history, presenting signs, results of routine blood tests, thyroid tests, cerebrospinal fluid (CSF) analysis and diagnostic imaging, and response to therapy were compiled. Results: Three Labrador Retrievers and one Labrador Retriever cross fit the inclusion criteria. All dogs were hypothyroid and severely hyperlipidemic. Neurologic signs included tetraparesis, central and peripheral vestibular signs, facial paralysis, and paraparesis. Two dogs had an acute history and rapid resolution of signs consistent with an infarct, the presence of which was confirmed in 1 of the dogs by magnetic resonance imaging. Two dogs had chronic histories of cranial neuropathies and paraparesis. One of these dogs had evidence of iliac thrombosis and atherosclerosis on ultrasound examination. All dogs improved with thyroid hormone supplementation. Clinical Relevance: Labrador Retrievers may be predisposed to the development of severe hyperlipidemia in association with hypothyroidism. One possible consequence of severe hyperlipidemia is the development of neurologic signs due to atherosclerosis and thromboembolic events.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".