MétaCan
Menu
← Back to cohort
Record W4205515100 · doi:10.31979/etd.w5fp-ccq6

Prediction of Financial Capacity using Diffusion Compartment Imaging

2021· dissertation· en· W4205515100 on OpenAlexfundno aff
Lok Yi Tai

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGenentechNational Institutes of HealthEisaiBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeNorthern California Institute for Research and EducationF. Hoffmann-La RocheUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsDiffusion MRIWhite matterCingulum (brain)NeuropsychologyMagnetic resonance imagingPsychologyCardiologyOrientation (vector space)DementiaCognitionMedicineNeuroscienceAudiologyCognitive psychologyFractional anisotropyInternal medicineDiseaseRadiologyMathematics

Abstract

fetched live from OpenAlex

Financial Capacity (FC) is the ability to manage one’s financial affairs, which is essential for autonomy and independence particularly for aging adults. Since dementia develops gradually, it is often difficult to detect the early signs that this cognitive dysfunction is developing This project aims to use Neurite orientation dispersion and density imaging (NODDI) to identify the white matter tracts that are associated with FC. Diffusion Tensor Images (DTI) and T1 Magnetic Resonance Images (MRI) of 18 Alzheimer’s Disease (AD) subjects, 47 Mild Cognitive Impaired (MCI) subjects, and 193 healthy control (CN) are compared to neuropsychological tests. Orientation Dispersion Index (ODI) values are derived using NODDI analysis of the DTI and MRI. This study found that the ODI values in the cingulum have the highest association with FC. In conclusion, our study suggested that the degradation of white matter and episodic memory dysfunction were most strongly associated with reduced FC.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.109
GPT teacher head0.360
Teacher spread0.252 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Neuroimaging Techniques and Applications→French-language works237,207→