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Neuropsychological outcomes after epilepsy surgery: Systematic review and pooled estimates

2011· review· en· W1491112993 on OpenAlexafffund
Elisabeth M. S. Sherman, Samuel Wiebe, Taryn B. Fay‐McClymont, José Francisco Téllez‐Zenteno, Amy Metcalfe, Lizbeth Hernández‐Ronquillo, Walter Hader, Nathalie Jetté

Bibliographic record

VenueEpilepsia · 2011
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsFoothills Medical CentreAlberta Children's HospitalUniversity of SaskatchewanUniversity of CalgaryAlberta Health Services
FundersCanadian Institutes of Health ResearchMilken Family FoundationAlberta Health ServicesAlberta Heritage Foundation for Medical ResearchUniversity of CalgaryAmerican Epilepsy Society
KeywordsEpilepsy surgeryNeuropsychologyEpilepsyVerbal fluency testCognitionNeuropsychological assessmentCognitive declineExecutive functionsPsychologyVerbal memoryCognitive reserveMeta-analysisVerbal learningMedicinePsychiatryClinical psychologyInternal medicineDiseaseDementiaCognitive impairment

Abstract

fetched live from OpenAlex

PURPOSE: Epilepsy surgery is a safe surgical procedure, but it may be associated with cognitive changes. Estimates of the risk of decline in specific neuropsychological domains after epilepsy surgery would assist surgical decision making in clinical practice. The goal of this study was to conduct a systematic review to derive pooled estimates of the rate of losses and gains in neuropsychological functions after epilepsy surgery, using empirically based methods for quantifying cognitive change. METHODS: An extensive literature search using PubMed, EmBase, and the Cochrane database was conducted, yielding 5,061 articles on epilepsy surgery, with 193 on neuropsychological outcomes (IQ, memory, language, executive functioning, attention, and subjective cognitive changes). KEY FINDINGS: Of these, 23 met final eligibility criteria, with 22 studies involving temporal surgery only. Key aspects of inclusion criteria were N ≥ 20 and use of reliable change index or standardized regression-based change estimates. In addition to the proportion of patients experiencing losses and gains in each individual test, a single pooled estimate of gains and losses for each cognitive domain was derived using a random effects model. Weighted estimates indicated a risk to verbal memory with left-sided temporal surgery of 44%, twice as high as the rate for right-sided surgery (20%). Naming was reduced in 34% of left-sided temporal patients, with almost no patients with gains (4%). Pooled data on IQ, executive functioning, and attention indicated few patients show declines post surgery, but a substantial rate of improvement in verbal fluency with left-sided temporal surgery (27%) was found. Self-reported cognitive declines after epilepsy surgery were uncommon, and gains were reported in some domains where losses were found on objective tests (i.e., verbal memory and language). Variations in surgical techniques did not appear to have a large effect on cognitive outcomes, except for naming outcomes, which appeared better with more conservative resections. Sensitivity to postoperative changes differed across visual memory tests, but not verbal memory tests. Few conclusions could be made regarding cognitive risks and benefits of extratemporal epilepsy surgery, or of epilepsy surgery in children. SIGNIFICANCE: In sum, epilepsy surgery is associated with specific cognitive changes, but may also improve cognition in some patients. The results provide base rate estimates of expected cognitive gains and losses associated with epilepsy surgery that may prove useful in clinical settings.

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)0.030
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.030
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.086
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.033
Bibliometrics0.0160.015
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.069
GPT teacher head0.371
Teacher spread0.302 · 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 designMeta-analysis
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

Citations534
Published2011
Admission routes2
Has abstractyes

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