Conversion between bromcresol green‐ and bromcresol purple‐measured albumin in renal disease
Bibliographic record
Abstract
BACKGROUND: Albumin measured by a bromcresol purple dye-binding assay (Alb(BCP)) agrees more closely with the gold standard of immunonephelometry than does bromcresol green (Alb(BCG)) measurement. Both tests are in current clinical use. A method for converting between the two would be useful. METHODS: We measured albumin by bromcresol green and bromcresol purple in 535 patients, 155 of whom had renal disease. We randomly divided data from the patients with renal disease into two equal-sized sets, and used one set to derive, and the remaining set to validate, a regression equation relating the two values. RESULTS: The relationship Alb(BCG)=5.5+Alb(BCP) performed very well in both the renal patient validation set and in the data from 380 unselected in-patients and out-patients. Intraclass correlations for agreement between measured Alb(BCG) and predicted Alb(BCG) was 0.98 in both analyses. CONCLUSIONS: The ability to convert between these measurements will be of use in clinical situations where the absolute value of the serum albumin is important, when data from laboratories using different methodologies must be combined, and in the application of the Modification of Diet in Renal Disease formula to estimate glomerular filtration rate in patients whose albumin has been measured by bromcresol purple.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".