New possibilities of overcoming pharmacoresistance in epilepsy
Bibliographic record
Abstract
Despite the presence of antiepileptic drugs with different mechanisms of action and application points, almost a third of patients still remain resistant to drug therapy. In such patients, the risk of physical and mental injuries, depression, premature death increases, and the quality and standard of living decreases.Objective. To study the mechanisms of formation of pharmacoresistance in epilepsy and to evaluate possible ways to overcome it based on the analysis of current scientific publications containing information on this topic.Results. The proposed pathophysiological mechanisms of the formation of drug resistance reflect the target hypothesis, the carrier hypothesis, pharmacokinetic theory and neural network theory. However, they are based on preclinical studies and do not have a comprehensive explanation for the appearance of this phenomenon. Surgical treatment remains the most studied and most commonly used approach. Both the usual resection of the epileptogenic part of the brain and new less crippling interventions are used: laser ablation and stereotactic radiosurgery. As an alternative, polytherapy schemes, invasive and non-invasive neurostimulation techniques, and diet therapy can be considered.Conclusions. Insensitivity to antiepileptic drugs remains a major problem in epileptology, and to overcome it, new methods are being sought and developed to influence the presumed pathogenetic targets of pharmacoresistant epileptogenesis. Vagus, deep, transcranial neurostimulation, stereotactic surgery, and laser ablation should be considered as new safe and potentially effective techniques.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".