Approach to overcome the bilirubin interference in the estimation of serum creatinine by Jaffe’s method
Bibliographic record
Abstract
Introduction: Bilirubin has negative interference on creatinine measurement by routinely used Jaffe’s Kinetic method. Hence this study was conducted to find out a possible solution to the problem of bilirubin interference. Simple chemical method has been validated for serum creatinine estimation which is free from bilirubin interference. The performance of this method has been compared with the high performing enzymatic method. Aim: To estimate the serum creatinine in samples having high bilirubin level. Materials and Methods: 60 serum samples with bilirubin value>2 mg/dl were included in the study. Samples were divided into 3 groups each containing 20 samples, based on the bilirubin value. Each group was divided into two subgroups of 10 samples each. Initially serum creatinine was estimated by both enzymatic method and Jaffe’s kinetic method. Later serum creatinine was measured once again in all the subgroups by both methods after the preincubation of sample with NaOH for 5 min and deproteinization with Trichloroacetic acid. The statistical analysis is done by t test. Results: There was a significant increase in the creatinine obtained by Jaffe’s method after deproteinization and preincubation of the sample when compared to the creatinine obtained by Jaffe’s method before preincubation and deproteinization. The creatinine estimated after the deproteinization and preincubation by Jaffe’s method are similar to the creatinine obtained by enzymatic method before deproteinization and preincubation. Conclusion: Preincubation and deproteinization are the two simple approaches which can rectify the problem of negative interference of the bilirubin in estimating creatinine by Jaffe’s method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".