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Record W4226162046 · doi:10.3389/fneur.2022.855332

Remote Memory in Epilepsy: Assessment, Impairment, and Implications Regarding Hippocampal Function

2022· review· en· W4226162046 on OpenAlexfundno aff
Sanya Rastogi, Kimford J. Meador, William Barr, Orrin Devinsky, Beth A. Leeman-Markowski

Bibliographic record

VenueFrontiers in Neurology · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersYork UniversityClinical Science Research and DevelopmentNew York UniversityU.S. Department of Veterans Affairs
KeywordsEpilepsyEpisodic memoryMemory impairmentNeuroscienceHippocampusTemporal lobePsychologySemantic memoryCognitive psychologyHippocampal formationMesial temporal lobe epilepsyVerbal memoryCognition

Abstract

fetched live from OpenAlex

Studies of epilepsy patients provide insight into the neuroscience of human memory. Patients with remote memory deficits may learn new information but have difficulty recalling events from years past. The processes underlying remote memory impairment are unclear and likely result from the interaction of multiple factors, including hippocampal dysfunction. The hippocampus likely has a continued role in remote semantic and episodic memory storage over time, and patients with mesial temporal lobe epilepsy (TLE) are at particular risk for deficits. Studies have focused on lateralization of remote memory, often with greater impairment in left TLE, which may relate to verbal task demands. Remote memory testing is restricted by methodological limitations. As a result, deficits have been difficult to measure. This review of remote memory focuses on evidence for its underlying neurobiology, theoretical implications for hippocampal function, and methodological difficulties that complicate testing in epilepsy patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.081
GPT teacher head0.387
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations4
Published2022
Admission routes1
Has abstractyes

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