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Record W2064091449 · doi:10.1159/000101908

Behavioural Measures in Frontotemporal Lobar Dementia and Other Dementias: The Utility of the Frontal Behavioural Inventory and the Neuropsychiatric Inventory in a National Cohort Study

2007· article· en· W2064091449 on OpenAlexaffabout
Mervin Blair, Andrew Kertesz, Nicole Davis-Faroque, Ging‐Yuek Robin Hsiung, Sandra E. Black, Rémi W. Bouchard, Serge Gauthier, Danilo Guzman, David B. Hogan, Kenneth Rockwood, Howard Feldman

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2007
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of OttawaDalhousie UniversitySt Joseph's Health CareUniversity of TorontoUniversity of CalgaryMcGill UniversityUniversity of British ColumbiaUniversité Laval
Fundersnot available
KeywordsFrontotemporal dementiaCohortFrontotemporal lobar degenerationDementiaPsychologyPsychiatryPsychopathologyCohort studyConstruct validityAlzheimer's diseaseClinical psychologyPsychometricsDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Distinguishing between patients with frontotemporal lobar dementia (FTLD) and other dementing illnesses remains a difficult task for many clinicians. In this study, we aimed to provide further evidence for the construct validity of the frontal behavioural inventory (FBI) and assess its utility in differentiating FTLD patients from other groups using data from the Canadian Collaborative Cohort of Related Dementias (ACCORD) study. METHOD: Baseline scores on the FBI and neuropsychiatric inventory (NPI) were compared among several clinical groups (n = 177). RESULTS: The FBI discriminated a higher percentage of FTLD patients (>75% correct classification) from Alzheimer's disease and other groups compared to the NPI (54.2%). CONCLUSION: This study provides good evidence for convergent validity between the FBI and NPI (r = 0.72), indicating that both measures capture similar psychopathology in this nationwide cohort.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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 teacher head, 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

Citations36
Published2007
Admission routes2
Has abstractyes

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