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Record W3190489139 · doi:10.1111/nan.12758

A systems‐level analysis highlights microglial activation as a modifying factor in common epilepsies

2021· article· en· W3190489139 on OpenAlexafffund
André Altmann, Mina Ryten, Martina Di Nunzio, Teresa Ravizza, Daniele Tolomeo, Regina H. Reynolds, Alyma Somani, Marco Bacigaluppi, Valentina Iori, Edoardo Micotti, Rossella Di Sapia, Milica Cerovic, Eleonora Palma, Gabriele Ruffolo, Juan A. Botía, Julie Absil, Saud Alhusaini, Marina K. M. Alvim, Pia Auvinen, Núria Bargalló, Emanuele Bartolini, Benjamin Bender, Felipe P. G. Bergo, Tauana Bernardes, Andrea Bernasconi, Neda Bernasconi, Boris C. Bernhardt, Karen Blackmon, Bárbara Braga, Maria Eugenia Caligiuri, Anna Calvo, Chad Carlson, Sarah J. A. Carr, Gianpiero L. Cavalleri, Fernando Cendes, Jian Chen, Shuai Chen, Andrea Cherubini, Luis Concha, Philippe David, Norman Delanty, Chantal Depondt, Orrin Devinsky, Colin P. Doherty, Martin Domín, Niels K. Focke, Sonya Foley, Wendy França, Antonio Gambardella, Renzo Guerrini, Khalid Hamandi, Derrek P. Hibar, Dmitry Isaev, Graeme D. Jackson, Neda Jahanshad, Reetta Kälviäinen, Simon S. Keller, Peter Kochunov, Raviteja Kotikalapudi, Magdalena Kowalczyk, Ruben Kuzniecky, Patrick Kwan, Angelo Labate, Sönke Langner, Matteo Lenge, Min Liu, Pascal Martin, Mario Mascalchi, Stefano Meletti, Marcia Morita‐Sherman, Terence J. O’Brien, José C. Pariente, Mark P. Richardson, Raúl Rodríguez‐Cruces, Christian Rummel, Taavi Saavalainen, Mira Semmelroch, Mariasavina Severino, Pasquale Striano, Thomas Thesen, Rhys H. Thomas, Manuela Tondelli, Domenico Tortora, Anna Elisabetta Vaudano, Lucy Vivash, Felix von Podewils, Jan Wagner, Bernd Weber, Roland Wiest, Clarissa Lin Yasuda, Guohao Zhang, Junsong Zhang, Costin Leu, Andreja Avberšek, Maria Thom, Christopher D. Whelan, Paul M. Thompson, Carrie R. McDonald, Annamaria Vezzani, Sanjay M. Sisodiya

Bibliographic record

VenueNeuropathology and Applied Neurobiology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersFP7 Ideas: European Research CouncilNational Institute of Biomedical Imaging and BioengineeringEpilepsy Research UKNational Institutes of HealthFondazione Antonio Carlo MonzinoMedical Research CouncilEpilepsy SocietyMedical Research Council CanadaEuropean CommissionUniversity College London Hospitals NHS Foundation TrustKaty Baggott FoundationNational Institute of Neurological Disorders and StrokeNational Institute for Health and Care ResearchIstituto di Ricerche Farmacologiche Mario Negri - IRCCS
KeywordsMicrogliaNeuroscienceEpilepsyNeuroimagingHuman brainCerebral cortexCell typeBiologyPathologyMedicineCellInflammationImmunology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.271
Teacher spread0.221 · 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

Citations54
Published2021
Admission routes2
Has abstractyes

Explore more

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