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Record W2896365221 · doi:10.1016/j.jalz.2018.06.2723

O2‐14‐06: DIFFERENCES BETWEEN SPORADIC AND FAMILIAL BEHAVIORAL VARIANT FTD IN ADVANCING RESEARCH AND TREATMENT FOR FTLD (ARTFL) CLINICAL RESEARCH CONSORTIUM

2018· article· en· W2896365221 on OpenAlexaff
Adam L. Boxer, Hilary W. Heuer, Ping Wang, Katya Rascovsky, Howard J. Rosen, Bradley F. Boeve, Murray Grossman, Giovanni Coppola, Brad C. Dickerson, Yvette Bordelon, Kimiko Domoto‐Reilly, Kelley Faber, Howard Feldman, Julie A. Fields, Jamie Fong, Tatiana Foroud, Nupur Ghoshal, N. 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, Rosa Rademakers, Erik D. Roberson, Marg Sutherland, Maria Carmela Tartaglia, Arthur W. Toga, Sandra Weıntraub, Emily Rogalskı, Zbigniew K. Wszołek

Bibliographic record

VenueAlzheimer s & Dementia · 2018
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsOccupational Cancer Research CentreUniversity of TorontoUniversity of British ColumbiaVancouver Coastal Health Research Institute
Fundersnot available
KeywordsFrontotemporal dementiaC9orf72Frontotemporal lobar degenerationMedicineDementiaNeuropsychologyClinical trialDiseasePediatricsPsychiatryInternal medicineCognition

Abstract

fetched live from OpenAlex

We aimed to compare the clinical and demographic features of sporadic and genetic forms of behavioral variant frontotemporal dementia (bvFTD) evaluated in the ARTFL/LEFFTDS projects. ARTFL is an 18 center clinical research consortium preparing for FTLD clinical trials that operates in conjunction with the Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS) project. Many new FTLD trials will focus on autosomal dominant familial FTLD (fFTLD) because it is possible to definitively determine individuals’ underlying pathology during life and because prevention trials may be possible in asymptomatic gene carriers. A major question is whether findings in fFTLD will translate to the more common sporadic FTLD syndromes, of which bvFTD is the most common syndrome. Clinical and neuropsychological ratings from the National Alzheimer's Coordinating Center Uniform Data Set (UDS) and FTLD modules were evaluated in patients meeting 2011 FTDC criteria for bvFTD evaluated in the ARTFL/LEFFTDS consortium between 2015-2017. Through December, 2017, 213 bvFTD were enrolled in ARTFL, including 136 sporadic cases (36.8% female; mean: 63.6±8.8 years) and 77 familial cases (47.9% female; 58.5±9.7 years). Familial bvFTD included 33 C9orf72, 20 MAPT, 4 GRN mutation carriers (confirmed or presumed) and 20 bvFTD with strong FTLD family history but no identifiable mutation. Sporadic and familial bvFTD did not differ in measures of disease severity such as CDR-FTLD-SB (8-item), MoCA, and CGI-S. Sporadic bvFTD were older (p<0.001) and had higher total NPI-Q scores (11.9±6.0 vs. 10.1±5.9, p =0.05). In patients with worse disease severity (CDR-FTLD-SB ≥ median, which was 8.0), MAPT mutation carriers had worse parkinsonism (UPDRS and PSPRS scores) compared with sporadic and other familial bvFTD patients. No differences in presence of hallucinations or delusions on NPI-Q were noted between C9orf72 bvFTD and other cohorts. Sporadic bvFTD cases are older and tend to have more severe neuropsychiatric symptoms than familial bvFTD, but are otherwise highly similar on the NACC UDS and FTLD module assessments. This supports the hypothesis that a clinically meaningful treatment response in familial bvFTD may be generalizable to sporadic bvFTD.

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)0.009
metaresearch head score (Gemma)0.011
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 score0.016
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.359
GPT teacher head0.495
Teacher spread0.136 · 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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