Supported liquid extraction offers improved sample preparation for aldosterone analysis by liquid chromatography tandem mass spectrometry
Bibliographic record
Abstract
BACKGROUND: To evaluate the accuracy and precision of a method for serum aldosterone using supported liquid extraction (SLE) for sample preparation instead of the more conventional liquid-liquid extraction (LLE) approach. METHODS: Two independently developed SLE-based LC-MS/MS methods for serum aldosterone (sample volumes 250 μl and 300 μl respectively) were compared to a modification of a previously reported LLE approach (sample volume 500 μl) in two method comparisons (n=75 and n=97). SLE analyses were performed at two separate centres. Precision was evaluated at a single site using human pools in head-to-head comparison between SLE and LLE. All analyses were performed on the ABSCIEX API-5000 LC-MS/MS system. RESULTS: At four increasing pool concentrations spanning 67-1060 pmol/l, total precision for SLE ranged from 6.8-4.1% compared with 11.1-4.3% for LLE. Differences did not reach statistical significance except at the lowest concentration where SLE was superior. Pasing Bablok regression comparisons were SLE=0.96×LLE-5.8 pmol/l (R(2)=0.985) and SLE=0.96×LLE-0.44 pmol/l (R(2)=0.969). CONCLUSIONS: For analysis of serum aldosterone on the ABSCIEX API-5000, SLE affords a smaller sample volume while maintaining the accuracy and precision performance of LLE. By avoiding specimen vortexing, SLE also allows for greater automation in the sample preparation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".