Utility of commercially available reagent test strips for estimation of blood urea nitrogen concentration and detection of azotemia in pet rabbits (Oryctolagus cuniculus) and ferrets (Mustela putorius furo)
Bibliographic record
Abstract
OBJECTIVE: ). SAMPLE: 65 blood samples from 53 rabbits and 71 blood samples from 50 ferrets of various health statuses. PROCEDURES: BUN concentrations were measured with a clinical laboratory biochemical analyzer and estimated with a reagent test strip. Results obtained with both methods were assigned to a BUN category (range, 1 to 4; higher categories corresponded to higher BUN concentrations). Samples with a biochemical analyzer BUN concentration ≥ 27 mg/dL (rabbits) or ≥ 41 mg/dL (ferrets) were considered azotemic. A test strip BUN category of 3 or 4 (rabbits) or 4 (ferrets) was considered positive for azotemia. RESULTS: Test strip and biochemical analyzer BUN categories were concordant for 46 of 65 (71%) rabbit blood samples and 58 of 71 (82%) ferret blood samples. Sensitivity, specificity, and accuracy of the test strips for detection of azotemia were 92%, 79%, and 82%, respectively, for rabbit blood samples and 80%, 100%, and 96%, respectively, for ferret blood samples. CONCLUSIONS AND CLINICAL RELEVANCE: Test strips provided reasonable estimates of BUN concentration but, for rabbits, were more appropriate for ruling out than for ruling in azotemia because of false-positive test strip results. False-negative test strip results for azotemia were more of a concern for ferrets than rabbits. Testing with a biochemical analyzer remains the gold standard for measurement of BUN concentration and detection of azotemia in rabbits and ferrets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".