Determination of the reference interval for urine kidney injury molecule-1 in 50 healthy cats
Bibliographic record
Abstract
Objectives The aim of the present study was to establish a reference interval (RI) for urine kidney injury molecule-1 (KIM-1) in healthy cats. Methods History, physical examination, blood pressure, and feline immunodeficiency virus and feline leukemia virus serology status were determined. A complete blood cell count, serum biochemical profile, urinalysis and kidney ultrasound were performed, and N-terminal pro-brain natriuretic peptide, total thyroxine (TT4) and urine KIM-1 were measured. An RI was calculated and the effect of age, sex, body condition score (BCS), blood pressure, symmetric dimethylarginine (SDMA), serum creatinine concentration (SCr), phosphorus, TT4, urine specific gravity (USG) and mid-sagittal kidney length on urine KIM-1 was evaluated using a general linear model. Results Of 69 recruited cats, 50 met the inclusion criteria. There were 35 male cats and 15 female cats, with a median age of 4.3 years (range 1.0–12.3), median weight of 5.11 kg (range 2.52–8.45) and median BCS of 6/9 (range 3–8). The median serum concentrations were SDMA 11.0 µg/dl (range 2–14), SCr 88.5 µmol/l (range 47–136), phosphorus 1.41 mmol/l (range 0.8–2.2) and TT4 32.0 nmol/l (range 17–51). Median USG was 1.057 (range 1.035–1.076), mid-sagittal left kidney length was 3.50 cm (range 2.94–4.45) and mid-sagittal right kidney length was 3.70 cm (range 3.06–4.55). The derived RI for urine KIM-1 was 0.02–0.68. USG was a significant ( P <0.001) predictor of urine KIM-1. Individually, age, sex, blood pressure, BCS, SDMA, SCr, phosphorus, TT4 and mid-sagittal kidney length were not significant predictors of urine KIM-1. In a multivariate model, if combined with USG, SDMA concentration was predictive ( P = 0.030) of urine KIM-1. Conclusions and relevance Urine concentration was significantly correlated with urine KIM-1, which will be an important consideration when interpreting findings in cats with potential kidney injury.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".