Evaluation of Predictors for the Diagnosis of Hyperthyroidism in Cats
Bibliographic record
Abstract
BACKGROUND: In humans, subclinical hyperthyroidism is diagnosed when serum thyroid hormone concentrations are within the reference range but thyroid stimulating hormone (TSH) concentration is subnormal. In a previous study, a higher prevalence of thyroid nodular disease was found in euthyroid geriatric cats with undetectable TSH (<0.03 ng/mL) compared to those with detectable TSH concentrations, suggesting subclinical hyperthyroidism might also exist in cats. HYPOTHESIS: Euthyroid cats with undetectable TSH concentrations have subclinical hyperthyroidism and may subsequently develop overt signs of hyperthyroidism. ANIMALS: One-hundred four client-owned cats. METHODS: In this prospective cohort study, euthyroid geriatric (≥ 9 years) cats were recruited during routine health checks. Plasma biochemistry was performed at baseline and every 6 months thereafter. Total thyroxine and TSH concentrations were determined annually. Short-term follow-up data (within 14 months of recruitment) were used to detect variables at entry that were predictive of the diagnosis of hyperthyroidism, using univariable analysis followed by multivariable logistic regression analysis. Log rank analysis was used to test the association of initial TSH concentration with diagnosis of hyperthyroidism during the total available follow-up. RESULTS AND CONCLUSIONS: Median (range) follow-up was 26 (0-54) months and annual incidence of hyperthyroidism during the study was 7.4%. Cats that became hyperthyroid within 14 months had higher ALKP activity (P = 0.02) and higher prevalence of goiter (P = .03) at baseline than controls. Cats with undetectable TSH at baseline (29/104; 28%) were significantly (P < .001) more likely to be diagnosed with hyperthyroidism. However, not all cats with undetectable TSH became hyperthyroid during the study.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".