Comparison between Immunoturbidimetry, Size-Exclusion Chromatography, and LC-MS to Quantify Urinary Albumin
Bibliographic record
Abstract
BACKGROUND: The accurate and precise measurement of urinary albumin is critical, since even minor increases are diagnostically sensitive indicators of renal disease, cardiovascular events, and risk for death. To gain insights into potential measurement biases, we systematically compared urine albumin measurements performed by LC-MS, a clinically available immunoturbidimetric assay, and size-exclusion HPLC. METHODS: We obtained unused clinical urine samples from 150 patients who were stratified by degrees of albuminuria (<20 mg/L, 20-250 mg/L, >250 mg/L) as determined by the immunoturbidimetric assay used in our clinical laboratory (Roche Hitachi 912). Urine albumin was then remeasured via LC-MS and HPLC (Accumin) assays. RESULTS: The immunoturbidimetric assay, calibrated using manufacturer-supplied serum-derived calibrators (Diasorin), underestimated albumin compared with LC-MS. After calibration with purified HSA, this immunoturbidimetric assay correlated well with LC-MS. HPLC overestimated albumin compared with both LC-MS and immunoturbidimetry. The current LC-MS and HPLC assays both performed poorly at concentrations <20 mg/L. CONCLUSIONS: Efforts are needed to establish gold-standard traceable calibrators for clinical assays. LC-MS is a specific method to quantify albumin in native urine when concentrations exceed 20 mg/L, and therefore could be employed for standardization among assays.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".