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What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons

2019· review· en· W2974165126 on OpenAlexaff
Claudio Babiloni, Katarzyna J. Blinowska, Laura Bonanni, Andrzej Cichocki, Willem de Haan, Claudio Del Percio, Bruno Dubois, Javier Escudero, Alberto Fernández, Giovanni B. Frisoni, Bahar Güntekin, Mihály Hajós, Harald Hampel, Emmanuel Ifeachor, Kerry Kilborn, Sanjeev Kumar, Kristinn Johnsen, Magnús Jóhannsson, Jaeseung Jeong, Fiona E. N. LeBeau, Roberta Lizio, Fernando Lopes da Silva, Fernando Maestú, William J. McGeown, Ian G. McKeith, Davide Vito Moretti, Flavio Nobili, John Olichney, Marco Onofrj, Jorge J. Palop, Michael J. Rowan, Fabrizio Stocchi, Heikki Tanila, Stefan Teipel, John‐Paul Taylor, Marco Weiergräber, Görsev Yener, Tracy L. Young‐Pearse, Wilhelmus Drinkenburg, Fiona Randall

Bibliographic record

VenueNeurobiology of Aging · 2019
Typereview
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsCentre for Addiction and Mental Health
FundersEngineering and Physical Sciences Research CouncilFondation pour la Recherche sur AlzheimerNational Institute on AgingHorizon 2020 Framework ProgrammeSorbonne UniversitéMinistero della SaluteEuropean Committee for Treatment and Research in Multiple SclerosisVertex PharmaceuticalsAlzheimer's AssociationAXA Research FundAgence Nationale de la Recherche
KeywordsNeuroscienceNeuropathologyElectroencephalographyElectrophysiologyMagnetoencephalographyNeuroimagingPsychologyNeurodegenerationDiseaseMedicinePathology

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.033
GPT teacher head0.312
Teacher spread0.279 · 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

Citations279
Published2019
Admission routes1
Has abstractno

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