The relevance of neuropsychiatric symptoms and cognitive problems in new-onset epilepsy : current knowledge and understanding
Bibliographic record
Abstract
Neurobehavioral and cognition problems are highly prevalent in epilepsy, but most research studies to date have not adequately addressed the precise nature of the relationship between these comorbidities and seizures. To address this complex issue and to facilitate collaborative, innovative research in the rising field of neurobehavioral comorbidities and cognition disturbances in new-onset epilepsy, international epilepsy experts met at the 3rd Halifax International Epilepsy Conference & Retreat at White Point, South Shore, Nova Scotia, Canada from September 18 to 20, 2014. This Conference Proceedings provides a summary of the conference proceedings. Specifically, the following topics are discussed: (i) role of comorbidities in epilepsy diagnosis and management, (ii) role of antiepileptic medications in understanding the relationship between epilepsy and neurobehavioral and cognition problems, and (iii) animal data and diagnostic approaches. Evidence to date, though limited, strongly suggests a bidirectional relationship between epilepsy and cognitive and psychiatric comorbidities. In fact, it is likely that seizures and neurobehavioral problems represent different symptoms of a common etiology or network-wide disturbance. As a reflection of this shared network, psychiatric comorbidities and/or cognition problems may actually precede the seizure occurrence and likely get often missed if not screened.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".