Ten things every neurologist needs to know about neuropsychological assessments and interventions in people with epilepsy
Bibliographic record
Abstract
This paper describes 10 core features of a neuropsychological assessment with the aim of helping neurologists understand the unique contribution the evaluation can make within the wider context of diagnostic methods in epilepsy. The possibilities, limitations and cautions associated with the investigation are discussed under the following headings. (1) A neuropsychological assessment is a collaborative investigation. (2) Assessment prior to treatment allows for the accurate assessment of treatment effects. (3) The nature of an underlying lesion and its neurodevelopmental context play an important role in shaping the associated neuropsychological deficit. (4) Cognitive and behavioural impairments result from the essential comorbidities of epilepsy which can be considered as much a disorder of cognition and behaviour as of seizures. (5) Patients' subjective complaints can help us understand objective cognitive impairments and their underlying neuroanatomy, resulting in improved patient care. At other times, patient complaints reflect other factors and require careful interpretation. (6) The results from a neuropsychological assessment can be used to maximize the educational and occupational potentials of people with epilepsy. (7) Not all patients are able to engage with a neuropsychological assessment. (8) There are limitations in assessments conducted in a second language with tests that have been standardized on different populations from that of the patient. (9) Adequate intervals between assessments maximize sensitivity to meaningful change. (10) Patients should be fully informed about the purpose of the assessment and have realistic expectations of the outcome prior to referral.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".