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Record W3048776145 · doi:10.3389/fneur.2020.00847

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures

2020· article· en· W3048776145 on OpenAlexafffundabout
Joel Ramirez, Melissa F. Holmes, Christopher J.M. Scott, Miracle Ozzoude, Sabrina Adamo, Gregory M. Szilagyi, Maged Goubran, Fuqiang Gao, Stephen R. Arnott, Jane M. Lawrence‐Dewar, Derek Beaton, Stephen C. Strother, Douglas P. Munoz, Mario Masellis, Richard H. Swartz, Robert Bartha, Sean Symons, Sandra E. Black

Bibliographic record

VenueFrontiers in Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsWestern UniversityHealth Sciences CentreQueen's UniversityThunder Bay Regional Research InstituteBaycrest HospitalSunnybrook Health Science CentreUniversity of Toronto
FundersFaculty of Health Sciences, Queen's UniversityLondon Health Sciences FoundationQueen's UniversityCentre for Addiction and Mental Health FoundationMcMaster UniversityTemerty Family FoundationUniversity of OttawaOntario Brain InstituteGovernment of Ontario
KeywordsNeuroimagingNeurovascular bundleFrontotemporal dementiaMedicineAmyotrophic lateral sclerosisDiseaseDementiaNeuroscienceAlzheimer's Disease Neuroimaging InitiativePhysical medicine and rehabilitationPathologyPsychology

Abstract

fetched live from OpenAlex

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online.

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)0.008
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: Methods · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.452

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.116
GPT teacher head0.392
Teacher spread0.276 · 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
GenreMethods

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

Citations39
Published2020
Admission routes3
Has abstractyes

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