Lithium heparinised blood-collection tubes give falsely low albumin results with an automated bromcresol green method in haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the cause of markedly low albumin values determined by a bromcresol green (BCG) method in patients on haemodialysis. METHODS: Serum and heparinised plasma from haemodialysis patients and normal controls were collected. Albumin was measured using Beckman bromcresol purple (BCP) and Roche BCG methods on the Beckman Synchron LX20. RESULTS: The albumin in heparinised plasma determined by a BCG method was 33.3% lower than that of the BCP method in a haemodialysis patient. The albumin values determined by the BCP method were comparable to those measured by immunonephelometric analysis for this patient. Significantly lower albumin levels were also observed in lithium heparin plasma by a BCG method compared to the BCP method in both non-renal patients (31.2+/-3.8 vs. 34.1+/-4.1 g/L, p<0.001, n=30) and haemodialysis patients (28.6+/-3.5 vs. 32.8+/-3.7 g/L, p<0.001, n=30). This negative bias was directly correlated with heparin concentrations in the plasma. The BCP method did not show this dose-dependent bias. CONCLUSIONS: Lithium heparin plasma can cause falsely low albumin values by an automated BCG method and the suitability of lithium heparin blood tubes should be carefully assessed for haemodialysis patients. The BCP method is free of this bias.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".