Diagnostic Accuracy of a Smartphone-Enabled Urinary Albumin-to-Creatinine Ratio Test for Detecting Chronic Kidney Disease Among At-Risk Populations
Bibliographic record
Abstract
Abstract Objective To evaluate the diagnostic accuracy of the FDA-cleared Minuteful Kidney Test (MKT), a home-based semi-quantitative urine albumin-to-creatinine ratio (uACR) test, compared to the Beckman Coulter AU 480 analyzer, a standard laboratory quantitative method, for detecting albuminuria in individuals with or at risk for chronic kidney disease (CKD). Methods Urine samples were obtained from individuals with risk factors for kidney damage. Each sample was analyzed for uACR using both the MKT and the laboratory standard method. Albuminuria was classified according to KDIGO guidelines: A1 (uACR ≤30 mg/g, “Normal to mildly increased”), A2 (uACR 30-300 mg/g, “Moderately increased”), and A3 (uACR ≥300 mg/g, “Severely increased”). Sensitivity, specificity, PPV, and NPV of MKT were calculated against the laboratory standard. Results Out of 615 collected urine samples, 555 met inclusion criteria. Albuminuria (uACR ≥30 mg/g) was detected in 24.9% of samples using the laboratory method (95% CI, 21.3–28.5%). The MKT demonstrated a sensitivity of 96.4%, specificity of 84.2%, NPV of 98.6%, and PPV of 66.8%. The test accurately categorized 85.6% of samples into the KDIGO albuminuria categories A1, A2, and A3, accurately identifying 100% of laboratory-confirmed A3 samples as abnormal. Conclusions The Minuteful Kidney Test shows high sensitivity, exceeding the guideline-required threshold (>85%, P<0.001), and robust specificity for detecting albuminuria in high-risk populations. This device offers potential to improve CKD screening and management and facilitate early intervention in primary care and community settings, where accurate rule-out capabilities are essential.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".