Utility of a novel point-of-care test for albuminuria in communities at high risk for chronic kidney disease in Thailand
Bibliographic record
Abstract
Introduction Chronic kidney disease (CKD) is a major public health concern, and early detection is crucial to prevent adverse outcomes. Albuminuria is an early marker and key prognostic marker in CKD, but reliable tools for its detection are limited particularly in low resource settings. We tested the utility of a novel, affordable point-of-care test (POCT) for albuminuria among high-risk individuals for CKD. Methods This is a community-based cross-sectional study covering 17 primary subdistrict healthcare units in Ban Phaeo District, Samut Sakhon Province, Thailand. The inclusion criteria were asymptomatic adult participants diagnosed with hypertension, diabetes and/or aged over 60 years. We measured serum creatinine and quantitative urine albumin–creatinine ratio (UACR) and administered POCT urine albumin strip test (Albii, K. BioSciences, Bangkok, Thailand) and urine dipstick test for protein. Participants with albuminuria or estimated glomerular filtration rate (eGFR) by CKD-EPI 2009 equation <60 mL/min/1.73 m2 were considered to have suspected CKD. We evaluated diagnostic performance of POCT urine albumin strip. Results Among 2307 participants, 489 (20.3%) participants had reduced eGFR and/or albuminuria. The median eGFR was 93.23 (87.82, 98.73) mL/min/m2, and the median UACR was 9.15 (5.09, 20.96) mg/g. The POCT urine albumin strip showed a sensitivity of 0.70, specificity of 0.97 and accuracy of 0.92 compared with the quantitative UACR. Conversely, the POCT urine dipstick for protein had poor sensitivity, positive predictive value and accuracy. Conclusion The urine albumin test strip is a highly effective tool to conduct point-of-care identification for early CKD among high-risk populations. Given the test’s diagnostic performance and ease of use, such test should be incorporated into health policy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".