Assay-dependent Effects of EDTA Contamination on Plasma Magnesium and Iron
Bibliographic record
Abstract
Abstract Background Inadvertent submission of ethylenediaminetetraacetic acid (EDTA) based plasma in place of heparinized plasma or contamination of serum or heparinized plasma with EDTA can alter select laboratory measurements. While factitious hypocalcaemia and abnormally low alkaline phosphatase (ALP) are recognized indicators of EDTA contamination, the impact of EDTA on Mg 2+ and Fe 2+ remains controversial. Method Herein, we derived the EDTA concentration required to cause a 50% decline (EC50 [EDTA] ) for Ca 2+ , ALP, Mg 2+ , and Fe 2+ across two methodologies (Roche Cobas c303 and Abbott Alinity c ) by varying EDTA concentration across plasma and serum pools. The concentration of EDTA required to exceed the allowable performance limit (APL [EDTA] ) was also evaluated across methods. Results Mg 2+ measured by an isocitrate dehydrogenase enzymatic method was resilient against EDTA, while Mg 2+ measured by xylidyl blue had an EC50 [EDTA] and APL [EDTA] of 0.78 – 1.18 mmol/L and 0.16 – 0.34 mmol/L, respectively. Fe 2+ measured with a ferene method was resilient to EDTA, whereas ferrrozine method indicated EC50 [EDTA] and APL [EDTA] of 5.6 – 8.86 mmol/L and 4.68 – 5.46 mmol/L, respectively. Ca 2+ and ALP exhibited larger EC50 [EDTA] on the Abbott Alinity c . Conclusion Hypomagnesemia and hypoferremia are not definitive markers of potential EDTA contamination, as Mg 2+ and Fe 2+ measurements show method-dependent susceptibilities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".