Current Issues in Measurement and Reporting of Urinary Albumin Excretion
Bibliographic record
Abstract
BACKGROUND: Urinary excretion of albumin indicates kidney damage and is recognized as a risk factor for progression of kidney disease and cardiovascular disease. The role of urinary albumin measurements has focused attention on the clinical need for accurate and clearly reported results. The National Kidney Disease Education Program and the IFCC convened a conference to assess the current state of preanalytical, analytical, and postanalytical issues affecting urine albumin measurements and to identify areas needing improvement. CONTENT: The chemistry of albumin in urine is incompletely understood. Current guidelines recommend the use of the albumin/creatinine ratio (ACR) as a surrogate for the error-prone collection of timed urine samples. Although ACR results are affected by patient preparation and time of day of sample collection, neither is standardized. Considerable intermethod differences have been reported for both albumin and creatinine measurement, but trueness is unknown because there are no reference measurement procedures for albumin and no reference materials for either analyte in urine. The recommended reference intervals for the ACR do not take into account the large intergroup differences in creatinine excretion (e.g., related to differences in age, sex, and ethnicity) nor the continuous increase in risk related to albumin excretion. DISCUSSION: Clinical needs have been identified for standardization of (a) urine collection methods, (b) urine albumin and creatinine measurements based on a complete reference system, (c) reporting of test results, and (d) reference intervals for the ACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".