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Record W2914072806 · doi:10.1007/s00401-019-01962-9

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD

2019· article· en· W2914072806 on OpenAlexaff
Cyril Pottier, Yingxue Ren, Ralph B. Perkerson, Matt Baker, Gregory D. Jenkins, Marka van Blitterswijk, Mariely DeJesus‐Hernandez, Jeroen van Rooij, Melissa E. Murray, Elizabeth Christopher, Shannon K. McDonnell, Zachary C. Fogarty, Anthony Batzler, Shulan Tian, Cristina T. Vicente, Billie J. Matchett, Anna M. Karydas, Ging‐Yuek Robin Hsiung, Harro Seelaar, Merel O. Mol, Elizabeth Finger, Caroline Graff, Linn Öijerstedt, Manuela Neumann, Peter Heutink, Matthis Synofzik, Carlo Wilke, Johannes Prudlo, Patrizia Rizzu, Javier Simón‐Sánchez, Dieter Edbauer, Sigrun Roeber, Janine Diehl‐Schmid, Bret M. Evers, Andrew King, Marsel Mesulam, Sandra Weıntraub, Changiz Geula, Kevin F. Bieniek, Leonard Petrucelli, Geoffrey L. Ahern, Eric M. Reiman, Bryan K. Woodruff, Richard J. Caselli, Edward D. Huey, Martin R. Farlow, Jordan Grafman, Simon Mead, Lea T. Grinberg, Salvatore Spina, Murray Grossman, David J. Irwin, Edward B. Lee, EunRan Suh, Julie S. Snowden, David Mann, Nilüfer Ertekin‐Taner, Ryan J. Uitti, Zbigniew K. Wszołek, Keith A. Josephs, Joseph E. Parisi, David S. Knopman, Ronald C. Petersen, John R. Hodges, Olivier Piguet, Ethan G. Geier, Jennifer S. Yokoyama, Robert A. Rissman, Ekaterina Rogaeva, Julia Keith, Lorne Zinman, Maria Carmela Tartaglia, Nigel J. Cairns, Carlos Cruchaga, Bernardino Ghetti, Julia Kofler, Oscar L. López, Thomas G. Beach, Thomas Arzberger, Jochen Herms, Lawrence S. Honig, Jean Paul Vonsattel, Glenda M. Halliday, John B. Kwok, Charles L. White, Marla Gearing, Jonathan D. Glass, Sara Rollinson, Stuart Pickering‐Brown, Jonathan D. Rohrer, John Q. Trojanowski, Vivianna Van Deerlin, Eileen H. Bigio, Claire Troakes, Safa Al‐Sarraj, Yan W. Asmann, Bruce L. Miller, Neill R. Graff‐Radford, Bradley F. Boeve, William W. Seeley, Ian R. Mackenzie, John C. van Swieten, Dennis W. Dickson, Joanna M. Biernacka, Rosa Rademakers

Bibliographic record

VenueActa Neuropathologica · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsToronto Western HospitalHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreWestern UniversityDiscovery CentreUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication DisordersNational Institute on AgingNational Institutes of HealthNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institute for Health and Care ResearchU.S. Department of Veterans Affairs
KeywordsFrontotemporal lobar degenerationGeneticsLocus (genetics)BiologyGenome-wide association studyC9orf72GenotypingDiseaseAmyotrophic lateral sclerosisAlleleGeneSingle-nucleotide polymorphismMedicineDementiaTrinucleotide repeat expansionFrontotemporal dementiaGenotypePathology

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.284
Teacher spread0.229 · 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

Citations128
Published2019
Admission routes1
Has abstractno

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