Clinical utility of therapeutic drug monitoring of antiepileptic drugs
Bibliographic record
Abstract
OBJECTIVE: To systematically review and evaluate the available evidence supporting or refuting clinical use of therapeutic drug monitoring (TDM) of antiepileptic drugs (AEDs) in patients with epilepsy. METHODS: We searched MEDLINE, Embase, BIOSIS, Cochrane, PubMed, Africa-Wide Information, Web of Science, and grey literature. Randomized controlled studies and observational studies that compared the clinical outcomes of TDM vs non-TDM were included. Two reviewers independently extracted the data. The primary outcome was seizure control; adverse effects were considered as secondary outcomes. The PROSPERO ID of this systematic review's protocol is CRD42018089925. RESULTS: Sixteen studies were identified meeting eligibility requirements. Four randomized controlled trials (RCTs), 1 meta-analysis, and 11 quasiexperimental (QE) studies were included in the systematic review. Results from the analysis of RCTs showed no significant positive effect of TDM on seizure outcome (only 25% positive effect of phenytoin). However, some of the QE studies found that TDM was associated with better seizure control or lower rates of adverse effects. The existing evidence from various designs has shown various methodological implications, which warrants inconclusive results and highlights the requirement of more number of studies in this line. CONCLUSIONS: If optimally implemented, TDM may enhance clinical care, particularly for phenytoin and other AEDs with complex pharmacokinetics. However, the ideal method for implementation is unclear, and serum drug levels should be considered in context with patient-reported clinical data regarding seizure control and adverse events.
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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 | 0.033 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".