Migration from RIA to LC-MS/MS for aldosterone determination: Implications for clinical practice and determination of plasma and urine reference range intervals in a cohort of healthy Belgian subjects
Bibliographic record
Abstract
Background: Aldosterone measurement is critical for diagnosis of primary aldosteronism and disorders of the renin-angiotensin system. We developed an LC-MS/MS method for plasma and urinary aldosterone and compared it to our RIA method. We present a reference interval study for a Belgian population. Methods: 68 plasma and 23 urine samples were assayed for as part of a method comparison. For the reference interval study, we enrolled 282 healthy Caucasian volunteers (114 Male: mean age 35 ± 11 y and 168 Female: mean age 42 ± 13 y). A subset of 139 healthy volunteers agreed to a 24-h urine collection. For the method validation, 5 plasma and 8 urine pools were run in triplicate and quadruplicate, respectively, on 3 different days. Results: Between-run imprecision (CV) was 2.8-5.1% for plasma and 4.5-8.6% for urine, except at the low urine concentration of 2.99 nmol/L where a CV of 15.4% was observed. The limit of quantitation was 0.04 nmol/L for plasma and 6.65 nmol/L for urine. Recoveries, based on spiking experiments into natural matrix, did not differ significantly from 100%. Regression comparisons showed that, on average, RIA generated results were 59% and 11% higher than LC-MS/MS for plasma and urine, respectively. The MS reference interval we propose for plasma aldosterone is 0.07 nmol/L-0.73 nmol/L for women and 0.04 nmol/L-0.41 nmol/L for men. No gender difference was observed for urine aldosterone. The reference interval was determined to be <60.94 nmol/day. Conclusions: The LC-MS/MS method was validated and reference intervals for plasma and urine were established. A significant bias between RIA and LC-MS/MS was noted.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".