Equine pituitary pars intermedia dysfunction: An international survey of veterinarians' approach to diagnosis, management, and estimated prevalence.
Bibliographic record
Abstract
The objectives of the present study were to determine if diagnosis and treatment of equine pituitary pars intermedia dysfunction (PPID) vary by geographic region and to report the prevalence of PPID in horses as observed by veterinarians across locations. An online questionnaire was developed for veterinarians who treat horses. Veterinary associations, especially equine specialty subgroups, were contacted and a survey link was sent to members of each organization. Generalized linear models were used to examine whether the method of diagnosis and treatment of this condition, as well as its reported prevalence, differed by geographic region. Veterinarians from 426 separate clinics in 20 countries returned surveys. Diagnosis of PPID varied by region, but was usually based on clinical signs and an adjunct endocrine test. Horses with PPID were treated medically by 63% of veterinarians and 75% of these used pergolide mesylate as treatment. The median prevalence estimated was 1% and this did not differ by geographic location. Half the veterinarians were caring for 5 or more animals with PPID. Overall, diagnostic approach differed in geographic regions. In general, European veterinarians were more likely than those in North America to diagnose PPID based on clinical signs alone, without using an adjunct laboratory test. Veterinarians reported that cost and management responsibilities were their clients' primary concerns associated with the long-term treatment of this disease, which indicates a need for additional treatment options for PPID.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".