A study examining the bias of albumin and albumin/creatinine ratio measurements in urine
Bibliographic record
Abstract
BACKGROUND: The objective of the study was to examine the bias of albumin and albumin/creatinine (ACR) measurements in urine. METHODS: Pools of normal human urine were augmented with purified human serum albumin to generate a series of 12 samples covering the clinical range of interest for the measurement of ACR. Albumin and creatinine concentrations in these samples were analyzed three times on each of 3 days by 24 accredited laboratories in Canada and the USA. Reference values (RV) for albumin measurements were assigned by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) comparative method and gravimetrically. Ten random urine samples (check samples) were analyzed as singlets and albumin and ACR values reported according to the routine practices of each laboratory. RESULTS: Augmented urine pools were shown to be commutable. Gravimetrically assigned target values were corrected for the presence of endogenous albumin using the LC-MS/MS comparative method. There was excellent agreement between the RVs as assigned by these two methods. All laboratory medians demonstrated a negative bias for the measurement of albumin in urine over the concentration range examined. The magnitude of this bias tended to decrease with increasing albumin concentrations. At baseline, only 10% of the patient ACR values met a performance limit of RV ± 15%. This increased to 84% and 86% following post-analytical correction for albumin and creatinine calibration bias, respectively. CONCLUSIONS: International organizations should take a leading role in the standardization of albumin measurements in urine. In the interim, accuracy based urine quality control samples may be used by clinical laboratories for monitoring the accuracy of their urinary albumin measurements.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".