Platform Session B: Clinical Neurophysiology/Clinical Epilepsy 3:00 p.m.–6:00 p.m.
Bibliographic record
Abstract
1 Jose F. Tellez‐Zenteno, 1 Scott B. Patten, and 1 Samuel Wiebe ( 1 Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada ) Rationale: Studies indicate that up to 50% of patients with epilepsy have mental health disorders, with mood, anxiety, and psychotic disturbances being the most common. However, the prevalence of psychiatric illnesses in persons with epilepsy in the general population varies, owing to differences in methods, population, case ascertainment, and heterogeneity of epilepsy syndromes. We assessed the prevalence of self‐reported, physician diagnosed mental health conditions associated with epilepsy in a large Canadian population health survey Methods: The Canadian Community Health Survey (CHS, N = 36,984) used probabilistic sampling to explore numerous aspects of mental health in the entire Canadian population, of whom 253 subjects had epilepsy. With sampling weights, the prevalence of epilepsy was 0.6%. Depression was ascertained with the Composite International Diagnostic Interview (Short Form). Other valid scales various aspects of psychiatric comorbidity. The prevalence of drug and alcohol use, and abnormal ideation were ascertained through personal interviews. We explored age specific prevalence of mental health problems in epilepsy Results: The lifetime prevalence of depression was 22.2% (95%CI 14.0–30.4%) compared with 12.2% in the general population. The prevalence of depression in people with epilepsy was higher than in the general population in younger, but not older (>64 years) age groups. There was a marked effect of age on the prevalence of major depression (higher in younger individuals). The prevalence of social phobia was 15.8% (8.4–23.2) in people with epilepsy and 8.1% (7.6–8.5) in the general population. The 12‐month prevalence of drug or alcohol dependence was not higher in people with epilepsy (3.0%) than in the general population (3.1%). Lifetime suicidal ideation was higher in patients with epilepsy 25.0% (95% CI 16.6–33.3) than in the general population 13.3% (95% CI 12.8–13.9) Conclusions: The prevalence of depression was considerable higher in younger people with epilepsy than in the general population. Social phobia and low indices of well being were more prevalent in epilepsy. We corroborated a high prevalence of suicidal ideation was in epilepsy patients. In contrast to other reports, we did not find a higher prevalence of alcohol and drug dependence in people with epilepsy. The complete analysis of mental health comorbidity will be presented 1 Miranda Geelhoed, 1 Anne Olde Boerrigter, 2 Peter R. Camfield, 1 Ada T. Geerts, 1 Willem Arts, 2 Bruce M. Smith, and 2 Carol S. Camfield ( 1 Department of Pediatric Neurology, Erasmus MC, Sophia Children's Hospital, Rotterdam, Netherlands ; and 2 Department of Pediatrics, Dalhousie University and IWK Health Centre, Halifax, NS, Canada ) Rationale: About 50–60% of children with epilepsy eventually outgrow their seizure disorder. A number of predictive factors have been statistically associated with remission but it is unclear how accurate these factors are when applied to an individual child. Two large prospective cohort studies of childhood epilepsy (Nova Scotia and the Netherlands) each developed a statistical model to predict long‐term outcome. We evaluated the accuracy of a prognostic model based on the two studies combined. Methods: A wealth of clinical and EEG variables were available for patients in both cohort studies. Data analyses with classification tree models and stepwise logistic regression produced predictive models for the combined dataset and the two separate cohorts. The resulting models were then externally validated on the opposite cohort. Remission was defined as no longer receiving daily medication for any length of time at the end of follow‐up. Results: The combined cohorts yielded 1055 evaluable patients. At the end of follow up (≥5 years in >96%), 622 (59%) were in remission. Using the combined data, the classification tree model and the logistic regression model predicted the outcome (remission or no remission) correctly in approximately 70% (sensitivity ∼72%, specificity∼65%, positive predictive value∼75%, negative predictive value ∼ 62%). The classification tree model split the data on epilepsy syndrome and age at first seizure. Independent statistically significant predictors in the logistic regression model were: seizure number before treatment, age at first seizure, absence seizures, epilepsy types of symptomatic generalized and symptomatic partial, pre‐existing neurological signs, intelligence and the combination of febrile seizures and cryptogenic partial epilepsy. When the prediction models from each cohort were cross‐validated on the opposite cohort, the outcome was predicted slightly less accurately than the model from the combined data. Conclusions: Based on currently available clinical and EEG variables, predicting the outcome of childhood epilepsy is difficult and appears to be incorrect in about one of every three patients. Predictions schemes are statistically robust but clinically relatively inaccurate. We suggest that clinicians should be cautious in applying prediction models when developing management strategies for individual children with epilepsy. 1 A. T. Berg, 2 B. G. Vickrey, 3 S. Smith, 3 F. M. Testa, 4 S. Shinnar, 3 S. R. Levy, 5 F. DiMario, and 3 B. Beckerman ( 1 BIOS, NIU, DeKalb, IL ; 2 Neurology, UCLA, Los Angeles, CA ; 3 Pediatrics, Yale, New Haven, CT ; 4 Neurology, Montefiore Hospital, Bronx, NY ; and 5 Neurology, CCMC, Hartford, CT ) Rationale: It is typically assumed that intractablility is evident soon after the onset of epilepsy. Retrospective histories from surgical patients, however, suggest that intractable seizures may not be evident for many years, particularly in partial epilepsy of childhood onset. Methods: In a community‐based study of 613 children in Connecticut with newly diagnosed epilepsy (1993–97) prospectively followed a median of 9 years, the timing of the appearance of intractable epilepsy from date of initial diagnosis was determined. Two definitions for intractable epilepsy were used: 1) “Strict:” 2 AED failures, ≥1 seizure/month for 18 months; 2) “Loose:” 2 AED failures. Differences in the timing of the appearance of intractability were examined as a function of type of epilepsy syndrome. Results: Eighty‐two children met the strict criteria for intractability: 38/294 (13%) of those with cryptogenic or symptomatic partial epilepsy (C/S‐PE), 35/67 (52%) of those with an epileptic encephalopathy (EE) and 9/241 (4%) of those with idiopathic or other forms of epilepsy (p < 0.0001). Eleven children followed<18 months were not assigned an outcome. Twenty‐five (30%) of the 82 intractable cases took >3 years to meet the strict criteria for intractability. The primary interest was in comparing EE and C/S‐PE groups. Five of 35 (14%) intractable cases in the EE group versus 17/38 (45%) in the C/S‐PE group met criteria at >3 years (p = 0.005). Loose criteria for intractability (2 AED failures) were met by 135 children. Of these, 32 (24%) met criteria >3 years after diagnosis: 1/46 in the EE group versus 25/69 in the C/S‐PE group (p < 0.0001). In the C/S‐PE group, 18/25 (72%) 25 who failed a second drug >3 years after diagnosis had experienced a 1+ year remission before the second drug failure. Conclusions: Poor seizure outcome is generally evident from the outset in the epileptic encephalopathies such as West, Lennox‐Gastaut syndrome. By contrast, the appearance of intractability may
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.688 | 0.427 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".