Systemic Analysis of Clinical, Neurophysiological and Psychometric Features of Patients with Pharmacoresistant Epilepsy
Bibliographic record
Abstract
INTRODUCTION: Drug-resistant epilepsy (DRE) is associated with a high incidence of comorbidities, psychiatric comorbidity, and early mortality in patients, which permits to consider epilepsy a systemic dysfunction of the body as a whole. AIM: Was to describe the features and relationships of clinical, neurophysiological and psychometric parameters of patients with DRE from a systemic standpoint. MATERIALS AND METHODS: A comprehensive clinical and neurophysiological study was conducted in 60 patients diagnosed with epilepsy, who were divided into 2 groups: 30 patients with DRE and 30 patients with remission of seizures. A clinical study was carried out including electroencephalography (EEG) with the calculation of the spectral power of the α-rhythm, the coherence coefficient and the cross-correlation coefficient for each pair of leads. The psychometric study included Eysenck, Strelau tests, the Taylor Anxiety Scale, the Freiburg Personality Inventory (FPI), the Toronto Alexithymia Scale, and the Self-Destructive Tendency Test. An analysis of correlations between clinical, psychometric and neurophysiological parameters in the initial state and with functional load was carried out. Based on the data obtained, graphical models were constructed. RESULTS: In patients with DRE, the clinical features of the disease, consisting in a longer duration of the disease and higher frequency of attacks, negatively correlated with the parameters of the spectral power of the α-rhythm, and with the levels of neuroticism and self-destructive behavior, and the low values of these characteristics can be associated with an unfavorable course of the disease. Rigid, systemic organization of psychological functions in patients with DRE, deficit of inhibitory processes in the nervous system were manifested in numerous correlations between psychometric parameters that negatively affect the quality of life, and also of psychometric parameters and spectral power of the EEG α-rhythm in different functional conditions. Numerous correlations between parameters may evidence a high level of conjugation of neurophysiological mechanisms with parameters in patients with DRE. CONCLUSION: Clinical, psychological and neurophysiological characteristics of patients with DRE are a reflection of a systemic phenomenon consisting in the mobilization of resources of the motivational-emotional, physiological spheres of patients not for external activity, but for inadequate defense reactions, which are subjective in nature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".