Therapeutic drug monitoring in the neurocritical care unit
Bibliographic record
Abstract
PURPOSE OF REVIEW: Various antiepileptics, sedative and anesthetic agents are used in the neurocritical care setting and therapeutic drug monitoring (TDM) has been proposed as a means to individualize dosing to ensure efficacy, avoid toxicity, and to account for drug-drug interactions. The purpose of this review is to highlight key articles relating to TDM published in the last 5 years with a focus on drug therapy for seizures, status epilepticus, and traumatic brain injury. RECENT FINDINGS: Current evidence supports TDM of first-generation antiepileptics, and free-level monitoring for phenytoin and valproic acid is recommended in the neurocritical care population. There are insufficient data to recommend routine TDM of second-generation antiepileptics at this time. In traumatic brain injury, routine TDM of barbiturate infusions appears to be of little value in guiding or evaluating patient response to therapy except to differentiate between drug-induced coma and brain death. Although TDM of sedative agents has been studied, the use of clinical sedation scales is preferred over TDM in evaluating a patient's level of sedation. SUMMARY: Therapeutic drug monitoring plays an important role in the care of patients in the neurocritical care setting but is applicable only to a limited number of drugs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".