Regional Implementation of Creatinine Measurement Standardization
Bibliographic record
Abstract
Because patients may receive care at multiple locations within a geographic area, serum creatinine measurements must be standardized across laboratories to enable comparisons of reported estimated glomerular filtration rate (eGFR). The results of a successful creatinine standardization program designed to minimize the contribution of laboratory error to the reporting of eGFR are reported; 107 laboratories, which tested creatinine on 124 analyzers from six different manufacturers, voluntarily participated. Each laboratory received a correction factor to apply to its creatinine measurements to standardize them to the isotope dilution mass spectrometry reference method. The adjusted values were then used to calculate eGFR using the Modification of Diet in Renal Disease (MDRD) equation. The standardization program reduced the average total error in the measurement of creatinine from 23.9 to 8.7% and the average analytical bias from 16.5 to 2.7%. Implementing this program on a larger scale could reduce the rate of incorrect classification of stage 3 chronic kidney disease by 84%.
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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".