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Record W2897685500 · doi:10.1016/j.jalz.2018.06.2719

O2‐14‐02: THE CLINICAL SPECTRUM OF FRONTOTEMPORAL LOBAR DEGENERATION IN NORTH AMERICA: BASELINE CHARACTERISTICS OF THE FIRST 912 PARTICIPANTS FROM THE ADVANCING RESEARCH AND TREATMENT IN FTLD (ARTFL) CLINICAL RESEARCH CONSORTIUM

2018· article· en· W2897685500 on OpenAlexaff
Hilary W. Heuer, Adam L. Boxer, Howard J. Rosen, Bradley F. Boeve, Murray Grossman, Brad C. Dickerson, Brian S. Appleby, Yvette Bordelon, Danielle Brushaber, Kimiko Domoto‐Reilly, Kelley Faber, Howard Feldman, Julie A. Fields, Jamie Fong, Tatiana Foroud, Nupur Ghoshal, Neill R. Graff‐Radford, Ging‐Yuek Robin Hsiung, Edward D. Huey, David J. Irwin, Kejal Kantarci, Daniel Kaufer, Anna M. Karydas, Diana Kerwin, David S. Knopman, John Kornak, Joel H. Kramer, Walter A. Kukull, Irene Litvan, Codrin Lungu, Ian R. Mackenzie, Mario F. Mendez, Bruce L. Miller, Chiadi U. Onyike, Alex Pantelyat, Madeline Potter, Rosa Rademakers, Eliana Marisa Ramos, Katherine P. Rankin, Katya Rascovsky, Erik D. Roberson, Marg Sutherland, Maria Carmela Tartaglia, Arthur W. Toga, Sandra Weıntraub, Zbigniew K. Wszołek

Bibliographic record

VenueAlzheimer s & Dementia · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsToronto Western HospitalUniversity Health NetworkUniversity of British Columbia
Fundersnot available
KeywordsFrontotemporal lobar degenerationMedicineFrontotemporal dementiaC9orf72PopulationDementiaNeuropsychologyMedical diagnosisDiseasePediatricsPsychiatryInternal medicinePathologyCognition

Abstract

fetched live from OpenAlex

ARTFL is a NIH-sponsored rare disease clinical research consortium involving 18 clinical centers that evaluate participants in order to 1) characterize the North American population of FTLD patients and 2) study longitudinal changes in familial FTLD (fFTLD) over one year. Key goals are to build clinical trial cohorts and to develop new clinical trial outcome measures including fluid biomarkers. Participants with FTLD spectrum disorders (bvFTD, svPPA, nfvPPA, FTD-ALS, CBD or PSP) or with strong family histories of FTLD undergo clinical and neuropsychological evaluations and blood draws for genetic and blood biomarker analyses. ARTFL is closely linked to the LEFFTDS project, sharing a common infrastructure and assessments. Asymptomatic fFTLD and a subset of sporadic cases undergo MRI. All ARTFL participants are genotyped for dementia-associated mutations. 912 (455 female (50%); 95% Caucasian) individuals were evaluated through December, 2017. In the 475 participants with sporadic FTLD, the most common diagnoses were bvFTD (30.5%) and PSP (20.8%), followed by svPPA (13%), nfvPPA (11.8%), CBS (9.8%) and sporadic FTD-ALS (3%). 45 (9.5%) individuals were referred to the project with FTLD diagnoses, but were determined by expert evaluation to have a different diagnosis. Of the 437 fFTLD participants, 35.7% were considered symptomatic (CDR>0) at their initial visit; the most prominent clinical phenotype was bvFTD (48.4%). Other diagnoses included FTD-ALS, MCI, and psychiatric disorders. The most common causative mutations identified in fFTLD were in C9ORF72 (including 3 in cases thought to be sporadic), followed by MAPT and GRN. 95 individuals with a strong FTLD family history, but no identifiable causative mutation in the family, have been enrolled to date. Expected differences in clinical and neuropsychological rating scales from the NACC UDS and FTLD modules were observed between diagnostic groups. ARTFL is building a substantial FTLD cohort at 18 North American research centers to support clinical research studies. Clinical, biomarker, genetic, imaging data and biospecimens from ARTFL and LEFFTDS are available to investigators worldwide via direct request and the NACC, LONI and NCRAD.

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 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)0.001
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 score0.947
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.129
GPT teacher head0.405
Teacher spread0.276 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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