MétaCan
Menu
Back to cohort
Record W3087294148 · doi:10.1177/0883073820957810

Youth Weigh In: Views on Advanced Neurotechnology for Drug-Resistant Epilepsy

2020· article· en· W3087294148 on OpenAlexaffabout
Farhad R. Udwadia, Patrick J. McDonald, Mary Connolly, Viorica Hrincu, Judy Illes

Bibliographic record

VenueJournal of Child Neurology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsNeuroDevNetUniversity of British Columbia
FundersNational Institute of Mental Health
KeywordsEpilepsyVagus nerve stimulationAutonomyContext (archaeology)Thematic analysisDrug Resistant EpilepsyPsychologyNeurostimulationQualitative researchMedicinePsychiatryNeurosciencePolitical scienceStimulationSociologyVagus nerve

Abstract

fetched live from OpenAlex

BACKGROUND: Epilepsy affects over 500,000 children in North America of whom 30% have drug-resistant epilepsy. Advancements with neurotechnologies show promising benefits, but the perceptions of these procedures by youth is unknown. METHODS: We conducted semistructured interviews with 10 youth in British Columbia, Canada who underwent procedures for drug-resistant epilepsy involving different forms of neurotechnology (subdural grids, vagus nerve stimulation, responsive neurostimulation). Interviews were analyzed using the constant comparative qualitative method. RESULTS: Four major thematic categories emerged from the interviews. Treatment values, impact of the disorder, personal context, and impact of neurotechnology. CONCLUSIONS: Besides the predictable goal of seizure reduction, a desire for autonomy and the importance of trust in the medical team emerged as dominant values within the 4 thematic categories that were explicit to the use of new neurotechnologies for the management of drug-resistant epilepsy.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.071
GPT teacher head0.320
Teacher spread0.249 · 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 designBench or experimental
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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Child NeurologySame topicVagus Nerve Stimulation ResearchFrench-language works237,207