Use of urinalysis during baseline diagnostics in dogs and cats: an open survey
Bibliographic record
Abstract
OBJECTIVES: To describe how veterinarians utilise and perform urinalyses for dogs and cats. MATERIALS AND METHODS: A survey, developed and distributed through the Veterinary Information Network, enlisted veterinarians who perform urinalyses for dogs and cats. Participants were directed to question banks based on whether urinalyses were performed in-house, by an outside diagnostic laboratory, or using an in-house automated instrument. Participants using multiple methods were directed to questions that related to the chosen methods. RESULTS: A total of 1059 predominantly first-opinion clinicians from the USA and Canada completed the survey. Participants performed urinalyses much less frequently than blood work during a routine examination. The most common factors preventing participants from performing a urinalysis with blood work included clients' financial constraints, difficulty obtaining urine and lack of perceived diagnostic need. The most common reasons for submission to a diagnostic laboratory included efficiency, more trusted results and convenience. Speed of obtaining results was the most common reason for performing urinalyses in-house. Of the participants who performed in-house urinalyses, fewer always performed a manual sediment examination (79%) as compared with urine-specific gravity (99%) and manual dipstick (87%). CLINICAL SIGNIFICANCE: This survey documents that urinalysis is often not used in senior patients as recommended by recent clinical guidelines for dogs and cats which can result in decreased diagnosis and impaired management of subclinical disease. There is significant variability in urinalysis methods despite veterinary guidelines promoting standardisation, and this could lead to inaccurate results.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".