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Record W2465242728 · doi:10.1016/j.jalz.2015.06.1683

P3‐309: Advancing research and treatment of frontotemporal lobar degeneration (ARTFL): Preparing for clinical trials for ftld in north america

2015· article· en· W2465242728 on OpenAlexaff
Adam L. Boxer, Howard J. Rosen, Bradley F. Boeve, Yvette Bordelon, Giovanni Coppola, Brad C. Dickerson, Nupur Ghoshal, N. R. Graff-Radford, Murray Grossman, Ging‐Yuek Robin Hsiung, Edward D. Huey, David J. Irwin, Irene Litvan, Daniel Kaufer, David S. Knopman, John Kornak, Ian R. Mackenzie, Mario F. Mendez, Bruce L. Miller, Chiadi U. Onyike, Rosa Rademakers, Maria Carmela Tartaglia, Sandra Weıntraub

Bibliographic record

VenueAlzheimer s & Dementia · 2015
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsFrontotemporal lobar degenerationC9orf72Frontotemporal dementiaClinical trialMedicinePrimary progressive aphasiaProgressive supranuclear palsySemantic dementiaNeuropsychologyAmyotrophic lateral sclerosisBiomarkerPsychologyDiseaseDementiaPathologyPsychiatryCognition

Abstract

fetched live from OpenAlex

The objective was to prepare for clinical trials of disease-modifying agents for frontotemporal lobar degeneration (FTLD) by building a network of investigators, recruitment tools, biomarker measurements, and training opportunities throughout North America. New treatments that target specific molecules (and genes) underlying FTLD including tau (MAPT), progranulin (GRN), TDP-43 (TARBP) and chromosome 9 open reading frame 72 (C9ORF72) are rapidly entering human clinical trials. Only a few randomized placebo controlled trials have ever been completed in FTLD, and little infrastructure exists to support new studies, especially trials defined based on molecular pathology as compared to clinical syndrome. Investigators at 14 centers pooled preliminary data regarding patient populations, potential enrollment, and barriers to clinical trial participation. We developed strategies for evaluating subjects using clinical measures and novel neuropsychological batteries, including the new FTLD module from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) and tests from a NINDS funded executive function battery (EXAMINER). We identified a need for funding investigator training and pilot projects focused on biomarker development. Two research projects funded through the NINDS and the National Center for Advancing Clinical and Translational Science (NCATS) Rare Disease Research Consortium Network. ARTFL is a partnership between academic investigators and patient advocacy groups including the AFTD, Cure PSP, Bluefield Project, Tau Consortium, CBD Solutions, and ADDF. One project will develop an online research registry and evaluates patients with sporadic FTLD syndromes with predictable underlying pathologies (progressive supranuclear palsy, semantic variant primary progressive aphasia, and frontotemporal dementia with amyotrophic lateral sclerosis) in order to identify clinical and biomarker measures to facilitate clinical trials. Novel neuropsychological batteries, genotyping, and clinical assessments will be performed in 650 participants. The second project focuses on longitudinal evaluation of both symptomatic and asymptomatic familial FTLD cases, using the same assessments and neuroimaging in 910 participants. The first pilot project focuses on AV1451 tau PET imaging in FTLD. Sites have IRB approval and will begin enrollment in spring 2015. Data will be publicly available to interested investigators. The data, recruitment tools, biomarkers, and training opportunities developed by ARTFL will facilitate new clinical trials in FTLD.

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)0.037
metaresearch head score (Gemma)0.010
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: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.004

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 head0.515
GPT teacher head0.526
Teacher spread0.011 · 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
GenreOther

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

Citations2
Published2015
Admission routes1
Has abstractyes

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