Utility, promise, and limitations of liquid chromatography‐mass spectrometry‐based therapeutic drug monitoring in precision medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) is typically referred to as the measurement of the concentration of drugs in patient blood. Although in the past, TDM was restricted to drugs with a narrow therapeutic range in order to avoid drug toxicity, TDM has recently become a major tool for precision medicine being applied to many more drugs. Through compensating for interindividual differences in a drug's pharmacokinetics, improved dosing of individual patients based on TDM ensures maximum drug effectiveness while minimizing side effects. This is especially relevant for individuals that present a particularly high intervariability in pharmacokinetics, such as newborns, or for critically/severely ill patients. In this article, we will review the applications for and limitations of TDM, discuss for which patients TDM is most beneficial and why, examine which techniques are being used for TDM, and demonstrate how mass spectrometry is increasingly becoming a reliable and convenient alternative for the TDM of different classes of drugs. We will also highlight the advances, challenges, and limitations of the existing repertoire of TDM methods and discuss future opportunities for TDM-based precision medicine.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it