Evidence for Efficacy of Combination of Antiepileptic Drugs in Treatment of Epilepsy
Bibliographic record
Abstract
Epilepsy is the tendency to have recurrent, unprovoked seizures. Although 70% of epileptic seizures can be controlled with monotherapy (treatment by single antiepileptic drug), a combination of two or more anti-epileptic drugs (AEDs) may be required to improve efficacy (seizure control) and tolerability. Polytherapy (treatment with two or more AEDs) can affect efficacies and side effects in additive, supra-additive (synergistic) or infra-additive fashion. The effect is considered supra-additive when the efficacy of the combination is greater than the sum of the individual drug efficacies, while it is considered infra-additive when the efficacy of the combination is less than the sum of the individual drug efficacies. Here, we have reviewed the available studies and evidences for the application of polytherapy in humans and animal models, to understand which combination of AEDs act as a synergistic polytherapy for epilepsy. We have searched the bibliographic databases MEDLINE and PubMed for studies conducted from 1950 to 2013 and have concluded that, although promising results from the experimental point of view support the combinations of topiramate separately with lamotrigine, gabapentin and felbamate, the most reliable evidence supports the use of valproate and lamotrigine, as this combination generates encouraging results in animal models. Though effectiveness of this combination is supported by human data, there is the possibility of increased side effects. The new drugs are all effective as add-on therapy; there is some evidence that at present, in clinical practice, levetiracetam and topiramate may be the most effective add-on therapies in partial and some generalized epilepsies. J Neurol Res. 2015;5(6):267-276 doi: http://dx.doi.org/10.14740/jnr356w
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".