Chronic Kidney Disease Screening Methods and Its Implication for Malaysia: An in Depth Review
Bibliographic record
Abstract
Chronic Kidney Disease has become a public health problem, imposing heath, social and human cost on societies worldwide. Chronic Kidney Disease remains asymptomatic till late stage when intervention cannot stop the progression of the disease. Therefore, there is an urgent need to detect the disease early. Despite the high prevalence of Chronic Kidney Disease in Malaysia, screening is still lacking behind. This review discusses the strengths and limitations of current screening methods for Chronic Kidney Disease from a Malaysian point of view. Diabetic Kidney Disease was chosen as focal point as Diabetes is the leading cause of Chronic Kidney Disease in Malaysia. Screening for Chronic Kidney Disease in Malaysia includes a urine test for albuminuria and a blood test for serum creatinine. Recent literature indicates that albuminuria is not always present in Diabetic Kidney Disease patients and serum creatinine is only raised after substantial kidney damage has occurred. Recently, cystatin C was proposed as a potential marker for kidney disease but this has not been studied thoroughly in Malaysia. Glomerular Filtration Rate is the best method for measuring kidney function and is widely estimated using the Modification of Diet for Renal Disease equation. Another equation, the Chronic Kidney Disease Epidemiology Collaboration Creatinine equation was introduced in 2009. The new equation retained the precision and accuracy of the Modification of Diet for Renal Disease equation at GFR < 60ml/min/1.73m2, showed less bias and improved precision at GFR>60ml/min/1.73m2. In Asian countries, adding an ethnic coefficient to the equation enhanced its performance. In Malaysia, a multi-ethnic Asian population, the Chronic Kidney Disease Epidemiology Collaboration equation should be validated and the Glomerular Filtration Rate should be reported whenever serum creatinine is ordered. Reporting estimated Glomerular Filtration Rate will help diagnose patients who would have been otherwise missed if only albuminuria and serum creatinine are measured.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".