Expanding the linear dynamic range in quantitative high performance liquid chromatography/tandem mass spectrometry by the use of multiple product ions
Bibliographic record
Abstract
A strategy for expanding the linear working range in bioanalysis using quantitative high performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) is presented. The strategy involves monitoring multiple product ions. Herein we demonstrate the strategy on a rat plasma assay for a proprietary experimental drug where the linear range is expanded from 2 to 4 orders of magnitude. A primary sensitive ion was monitored to obtain a high sensitivity range calibration curve (0.400 to 100 ng/mL) while a less sensitive secondary ion was monitored to obtain a low sensitivity range calibration curve (90.0 to 4000 ng/mL). Each calibration curve gave acceptable linearity (r >0.990). Quality control samples at low, mid and high levels within each calibration curve demonstrated acceptable precision and accuracy (within 20% for all levels). The technique was successfully applied to rat pre-clinical sample analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".