Medical management and monitoring of the hyperthyroid cat: a survey of UK general practitioners
Bibliographic record
Abstract
Feline hyperthyroidism is commonly diagnosed in general practice. This study assessed the opinions and experiences of UK general practitioners (GPs) regarding the management of feline hyperthyroidism. This included an evaluation of preferred treatment modalities and the monitoring of medically treated cats in relation to thyroxine (T4) level, co-morbid disease and adverse drug reactions. Six hundred and three GPs completed an online questionnaire comprising 34 questions. Oral medication was the most commonly preferred treatment option (65.7% of respondents), followed by thyroidectomy (27.5%) and then radioiodine (5.5%). When cost of treatment was eliminated as a consideration factor, significantly more respondents selected radioiodine (40.5%, P <0.001). Concerning target total T4 levels during medical management, 48.4% aimed for the lower half of the reference interval (RI), 32.3% anywhere within RI, 13.1% within the top half of RI and 0.5% above the RI; 3.4% evaluated efficacy by physical assessment only. In the presence of chronic kidney disease (CKD) respondents were significantly more likely to target total T4 levels within the upper half of the RI (40.3%) or above it (9.8%) when compared with targets for routine cases (P <0.001). Assessment for unmasking of CKD after initiating treatment or for hypertension was not consistently performed. Variability in monitoring strategies may result in CKD and hypertension remaining undetected, inadequate suppression of T4 levels in cats with concurrent CKD and delayed recognition of potentially significant haematological abnormalities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".