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Record W4390193506 · doi:10.1002/alz.076190

Analysis of person recognition deficits in genetic frontotemporal dementia

2023· article· en· W4390193506 on OpenAlexaff
Emily Todd, Arabella Bouzigues, Phoebe H. Foster, Eve Ferry‐Bolder, Georgia Peakman, Martina Bocchetta, David M. Cash, Caroline Greaves, Rhian S. Convery, John C. van Swieten, Lize C. Jiskoot, Harro Seelaar, Fermín Moreno, Raquel Sánchez‐Valle, Robert Laforce, Caroline Graff, Mario Masellis, Maria Carmela Tartaglia, James B. Rowe, Barbara Borroni, Elizabeth Finger, Matthis Synofzik, Daniela Galimberti, Rik Vandenberghe, Alexandre de Mendonça, Christopher Butler, Alexander Gerhard, Simon Ducharme, Isabelle Le Ber, Pietro Tiraboschi, Isabel Santana, Florence Pasquier, Johannes Levin, Markus Otto, Sandro Sorbi, Jonathan D. Rohrer, Lucy L. Russell

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteWestern UniversityUniversity of TorontoOccupational Cancer Research CentreHealth Sciences CentreSunnybrook Health Science CentreUniversité Laval
Fundersnot available
KeywordsFrontotemporal dementiaFrontotemporal lobar degenerationC9orf72PsychologySemantic dementiaClinical Dementia RatingAudiologyDementiaInternal medicineClinical psychologyMedicineOncologyPsychiatryCognitionCognitive impairmentDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Semantic and socioemotional knowledge, including person recognition, can be altered in frontotemporal dementia (FTD), and is often associated with the right temporal lobe variant. Using data from the Genetic FTD Initiative, we investigated person recognition deficits in genetic FTD. METHOD: 901 GENFI participants (279 mutation negative controls, 280 C9orf72 mutation carriers (MCs), 101 MAPTMCs and 241 GRN MCs) were grouped using the Clinical Dementia Rating scale plus National Alzheimer's Coordinating Centre Frontotemporal Lobar Degeneration (CDR plus NACC FTLD) global score where 0 denotes asymptomatic, 0.5 as prodromal, and 1+ as mild to severe symptoms (C9orf72: 135 = 0, 48 = 0.5, 97 = 1+; GRN: 143 = 0, 35 = 0.5, 63 = 1+; MAPT: 50 = 0, 20 = 0.5, 31 = 1+). Person recognition (PR) was assessed using a single question within a structured clinical questionnaire, scoring the ability to recognise people who should be familiar by face or voice to them, with a value between 0 (absent) to 3 (severe), similar to the CDR scale. The percentage of participants with PR deficits was calculated for each group. Logistic regression with bootstrapping compared the PR score between groups with age, gender, and education as covariates. RESULT: 16.1% of C9orf72 MCs (0 = 0.7%, 0.5 = 2.1%, 1+ = 44.3%), 7.5% of GRN (0 = 0.0%, 0.5 = 8.6%, 1+ = 23.8%) and 17.8% of MAPT carriers (0 = 2%, 0.5 = 10%, 1+ = 48.4%) showed PR deficits. Mean (standard deviation) severity in each group was: C9orf72 0 = 0.0(0.0), 0.5 = 0.0(0.1), 1+ = 0.6(0.9); GRN 0 = 0.0(0.0), 0.5 = 0.0(0.1), 1+ = 0.2(0.6); MAPT 0 = 0.0(0.2), 0.5 = 0.1(0.3), 1+ = 0.6(0.8). Each of the symptomatic genetic groups had a significantly greater PR deficit than the control group (p<0.001), with the prodromal MAPT (p = 0.006) and GRN (p<0.001) groups also showing a greater impairment than controls. There was a trend to significance in the C9orf72asymptomatic and prodromal groups compared with controls (p = 0.058 and p = 0.059 respectively). Symptomatic C9orf72 and MAPT carriers showed greater impairment than the symptomatic GRN carriers (both p = 0.005). CONCLUSION: Person recognition is a key early marker of disease in some individuals with genetic FTD and further imaging analyses will help to reveal the underlying mechanism of this deficit.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.060
GPT teacher head0.321
Teacher spread0.261 · 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
Published2023
Admission routes1
Has abstractyes

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