Determination of serum aldosterone by liquid chromatography and tandem mass spectrometry: a liquid–liquid extraction method for the ABSCIEX API-5000 mass spectrometry system
Bibliographic record
Abstract
AIMS: Accurate serum aldosterone determination is critical to the screening and diagnosis of primary aldosteronism, the localisation of aldosterone producing tumours, and the investigation of other disorders of the renin-angiotensin system. Mass spectrometry offers a means to overcome problems with method-dependent bias between competitive immunoassays for aldosterone. The authors have developed a simple, sensitive and precise liquid-liquid extraction aldosterone method for the ABSCIEX API-5000 liquid chromatography and tandem mass spectrometry (LC-MS/MS) system. METHODS: Using d7-aldosterone internal standard, 500 μl of sample is extracted with 2500 μl of methyl tertbutyl ether followed by dry-down, reconstitution and LC-MS/MS analysis in ESI negative mode. Method validation was undertaken using standard approaches and comparison made against a commercial radioimmunoassay. Accuracy was assessed using EQA material with assigned aldosterone concentrations. RESULTS: The assay was linear up to 3420 pmol/l (LOQ=50 pmol/l, LOD<22 pmol/l). Total CVs were ≤5% for concentrations ≥120 pmol/l and 10% at the LOQ. Mean accuracy was 98.5% against GCMS assigned material. CONCLUSION: The authors present a precise, sensitive and simple aldosterone method suitable for routine clinical use that requires no solid phase extraction or specialised ion sources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".