Medical management and monitoring of the hyperthyroid cat: a survey of UK general practitioners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it