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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".