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Record W3107471114 · doi:10.1016/j.yebeh.2020.107658

Evaluating risk to people with epilepsy during the COVID-19 pandemic: Preliminary findings from the COV-E study

2020· article· en· W3107471114 on OpenAlexafffund
Jennifer Thorpe, Samantha Ashby, Asma Hallab, Ding Ding, Maria Emília Cosenza Andraus, Patricia Dugan, Piero Perucca, Daniel J. Costello, Jacqueline A. French, Terence J. O’Brien, Chantal Depondt, Danielle M. Andrade, Robin Sengupta, Norman Delanty, Nathalie Jetté, Charles R. Newton, Martin J. Brodie, Orrin Devinsky, J. Helen Cross, Josemir W. Sander, Jane Hanna, Arjune Sen

Bibliographic record

VenueEpilepsy & Behavior · 2020
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersNIHR Oxford Biomedical Research CentreNational Health and Medical Research CouncilEngineering and Physical Sciences Research CouncilMedical Research CouncilEpilepsy SocietyNational Institutes of HealthRoyal Australasian College of PhysiciansEpilepsy Research UKEpilepsy ActionNational Institute of Neurological Disorders and StrokeGreat Ormond Street Hospital CharityDravet Syndrome UKNational Institute for Health and Care ResearchEpilepsy Research Program of the Ontario Brain InstituteMonash UniversityJoint Information Systems CommitteeSUDEP ActionUniversity College London Hospitals NHS Foundation TrustRoyal College of PhysiciansEpilepsy FoundationWaterloo FoundationDravet Syndrome FoundationPatient-Centered Outcomes Research Institute
KeywordsEpilepsyPandemicMental healthComorbidityPsychiatryMedicineAffect (linguistics)Social isolationIsolation (microbiology)StressorCoronavirus disease 2019 (COVID-19)Health carePsychologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.393
Teacher spread0.290 · 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

Citations52
Published2020
Admission routes2
Has abstractno

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