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Record W2042197019 · doi:10.1159/000070853

Quality of Life after Vagus Nerve Stimulation for Intractable Epilepsy: Is Seizure Control the Only Contributing Factor?

2003· article· en· W2042197019 on OpenAlexaff
Richard S. McLachlan, Mark Sadler, Neelan Pillay, A. Guberman, Michael Jones, Samuel Wiebe, Jack Schneiderman

Bibliographic record

VenueEuropean Neurology · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of OttawaUniversity of TorontoDalhousie UniversityWestern University
Fundersnot available
KeywordsVagus nerve stimulationEpilepsyVagus nerveMedicineAnesthesiaQuality of life (healthcare)StimulationProspective cohort studyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

We assessed the impact of vagus nerve stimulation on a cohort of patients with intractable epilepsy. A 1-year prospective trial of vagus nerve stimulation for intractable epilepsy was done in 26 patients. Seizure frequency, anti-epileptic drugs, and quality of life were assessed using QOLIE-89, ELDQOL, and a Likert scale of impact of treatment. Seizures were reduced by more than 50% in 19% of the patients, by less than 50% in 46%, and were unchanged in 35% of them. Antiepileptic drugs were reduced in 43% of the patients. There was a significant improvement in the mean overall QOLIE-89 score and other measures of quality of life, but these did not correlate with changes in seizure frequency. Subjective improvement occurred in 84% of the patients. The quality of life improves in some patients following vagus nerve stimulation for intractable epilepsy. The favorable effects of this treatment may be attributable to additional factors besides seizure control which in this study was modest.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.337
Teacher spread0.270 · 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 designObservational
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

Citations46
Published2003
Admission routes1
Has abstractyes

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