Correlation of thyroid hormone measurements with thyroid stimulating hormone stimulation test results in radioiodine-treated cats
Bibliographic record
Abstract
BACKGROUND: (RAI) treatment of hyperthyroid cats and can be diagnosed using the thyroid stimulating hormone (TSH) stimulation test. OBJECTIVES: To assess the effect of noncritical illness on TSH stimulation test results in euthyroid and RAI-treated cats. To assess the correlation of low total-thyroxine (tT4), low free-thyroxine (fT4), and high TSH concentrations with TSH stimulation test results. ANIMALS: Thirty-three euthyroid adult cats and 118 client-owned cats previously treated with RAI. METHODS: Total-thyroxine, fT4, and TSH were measured, and a TSH stimulation test was performed in all cats. Euthyroid control cats were divided into apparently healthy and noncritical illness groups. RAI-treated cats were divided into RAI-hypothyroid (after-stimulation tT4 ≤ 1.5 μg/dL), RAI-euthyroid (after-stimulation tT4 ≥ 2.3 μg/dL OR after-stimulation tT4 1.5-2.3 μg/dL and before : after tT4 ratio > 1.5), and RAI-equivocal (after stimulation tT4 1.5-2.3 μg/dL and tT4 ratio < 1.5) groups. RESULTS: Noncritical illness did not significantly affect the tT4 following TSH stimulation in euthyroid (P = .38) or RAI-treated cats (P = .54). There were 21 cats in the RAI-equivocal group. Twenty-two (85%) RAI-hypothyroid cats (n = 26) and 10/71 (14%) of RAI-euthyroid cats had high TSH (≥0.3 ng/mL). Twenty-three (88%) RAI-hypothyroid cats had low fT4 (<0.70 ng/dL). Of the 5 (7%) RAI-euthyroid cats with low fT4, only one also had high TSH. Only 5/26 (19%) RAI-hypothyroid cats had tT4 below the laboratory reference interval (<0.78 μg/dL). CONCLUSIONS AND CLINICAL RELEVANCE: The veterinary-specific chemiluminescent fT4 immunoassay and canine-specific TSH immunoassay can be used to aid in the diagnosis of iatrogenic hypothyroidism in cats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".