A saliva urea test strip for use in feline and canine patients: a pilot study
Bibliographic record
Abstract
We evaluated a saliva urea test strip (Kidney-Chek; SN Biomedical), as a rapid, noninvasive method to screen for azotemia. The test is a semiquantitative method that assesses 7 levels of saliva urea concentration, and indirectly serum urea, from <3 to >17 mmol/L. Ninety-two dogs (14 azotemic) with serum urea of 1.3–37 mmol/L and 56 cats (16 azotemic) with serum urea of 4.1–89.3 mmol/L were enrolled. A positive correlation was found for saliva urea against serum urea in each species (dogs: r s = 0.30, p < 0.005; cats: r s = 0.50, p < 0.001). After turning the semiquantitative data into continuous data by attributing to each level the midpoint of the described range, a receiver operating characteristic curve analysis showed good performance for detecting serum urea above the upper limit of the laboratory RI (dogs: 2.1–11.1 mmol/L; cats: 5–12.9 mmol/L), with an area under the curve of 0.81 in dogs and 0.83 in cats. We recommend that the test be used as an exclusion test, given that it cannot confidently confirm azotemia with higher test results. Additional investigations are recommended for dogs with a test strip reading of ≥9–11 mmol/L and for cats with a test strip reading of ≥12–14 mmol/L.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".