Urinary cystatin B differentiates progressive versus stable IRIS Stage 1 chronic kidney disease in dogs
Bibliographic record
Abstract
BACKGROUND: Early identification of dogs with progressive vs stable chronic kidney disease (CKD) might afford opportunity for interventions that would slow progression. However, currently no surrogate biomarker reliably predicts CKD progression. HYPOTHESIS/OBJECTIVES: Urinary cystatin B (uCysB), a novel kidney injury biomarker, predicts progressive disease in International Renal Interest Society (IRIS) CKD Stage 1. ANIMALS: Seventy-two dogs, including 20 dogs from 4 university centers with IRIS CKD Stage 1, with IDEXX symmetric dimethylarginine (SDMA) concentration up to 17 μg/dL and no systemic comorbidities, and 52 clinically healthy staff-owned dogs from a fifth university center. METHODS: A multicenter prospective longitudinal study was conducted between 2016 and 2021 to assess uCysB concentration in IRIS CKD Stage 1 and control dogs. Dogs were followed to a maximum of 3 years (control) or 25 months (CKD). Stage 1 IRIS CKD was classified as stable or progressive using the slope of 1/SDMA, calculated from 3 timepoints during the initial 90-day period. Dogs with slope above or below -0.0007 week × dL/μg were classified as stable or progressive, respectively. Mixed effects modeling was used to assess the association between uCysB and progression rate. RESULTS: Estimates of first visit uCysB results predictive of active ongoing kidney injury based on the mixed effects models were 17 ng/mL for control, 24 ng/mL for stable CKD, and 212 ng/mL for progressive CKD (P < .001). CONCLUSIONS AND CLINICAL IMPORTANCE: Urinary cystatin B differentiated stable vs progressive IRIS CKD Stage 1. Identification of dogs with progressive CKD may provide an opportunity for clinicians to intervene early and slow progression rate.
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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 | 0.002 |
| 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 | 0.001 | 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".