Quality, origins and limitations of common therapeutic drug reference intervals
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) is used to manage drugs with a narrow window between effective and toxic concentrations. TDM involves measuring blood concentrations of drugs to ensure effective therapy, avoid toxicity and monitor compliance. Common drugs for which TDM is used include aminoglycosides for infections, anticonvulsants to treat seizures, immunosuppressants for transplant patients and cardiac glycosides to regulate cardiac output and heart rate. An essential element of TDM is the provision of accurate and clinically relevant reference intervals. Unlike most laboratory reference intervals, which are derived from a healthy population, TDM reference intervals need to relate to clinical outcomes in the form of efficacy and toxicity. This makes TDM inherently more difficult to develop as healthy individuals are not on therapy, so there is no "normal value". In addition, many of the aforementioned drugs are old and much of the information regarding reference intervals is based on small trials using methods that have changed. Furthermore, individuals have different pharmacokinetics and drug responses, particularly in the context of combined therapies, which exacerbates the challenge of universal TDM targets. This focused review examines the origins and limitations of existing TDM reference intervals for common drugs, providing targets where possible based on available guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".