Diagnostic Challenges in the Neuropsychology of Epilepsy: Report of the <scp>ILAE</scp> Neuropsychology Task Force Diagnostic Methods Commission: 2021–2025
Bibliographic record
Abstract
Increasingly, it has been recognized that non-seizure-related factors influence how people with epilepsy perform on neuropsychological tests. Therefore, neuropsychologists need to recognize the constellation of factors that can contribute to the neurocognitive presentation of a person with epilepsy and consider these factors in the interpretation of their assessment results. In this paper, we highlight common scenarios prompting the need to account for such factors when conducting and interpreting neuropsychological assessments. To illustrate these complexities, a case study is presented of a woman with early onset epilepsy complicated by a traumatic brain injury in adulthood who was a candidate for epilepsy surgery. A discussion of the need to consider neurodevelopmental, psychiatric, and psychosocial factors in conducting an assessment and arriving at a sound interpretation of results is presented. Clinical take-away points are offered for guidance in undertaking such assessments.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".