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Record W7074042689

The relevance of neuropsychiatric symptoms and cognitive problems in new-onset epilepsy : current knowledge and understanding

2015· other· en· W7074042689 on OpenAlexaboutno aff

Bibliographic record

VenueTU/e Research Portal (Eindhoven University of Technology) · 2015
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEpilepsyCognitionRelevance (law)EtiologyComorbidityEpilepsy syndromes
DOInot available

Abstract

fetched live from OpenAlex

<p>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.</p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.309
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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