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Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements

2018· article· en· W2894201785 on OpenAlexafffund
Hyunwoo Lee, Kunio Nakamura, Sridar Narayanan, Robert A. Brown, Douglas L. Arnold

Bibliographic record

VenueNeuroImage · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchAbbVie CanadaNational Institutes of HealthServierMultiple Sclerosis Society of CanadaAlkermesFonds de Recherche du Québec - SantéAcorda TherapeuticsImmune Tolerance NetworkMyelin Repair FoundationPfizerBiogenBioClinicaEisai KoreaF. Hoffmann-La RocheCelgeneMultiple Sclerosis SocietyNovartis Pharmaceuticals CorporationBristol-Myers Squibb FoundationU.S. Department of DefenseEli Lilly and CompanyRocheAdelphi UniversityTakeda Pharmaceutical CompanyAbbVieSanofi GenzymeFonds de Recherche du Québec-Société et CultureGenzymeGenentechNational Multiple Sclerosis SocietyAlzheimer's Drug Discovery FoundationGE HealthcareAlzheimer's Disease Neuroimaging InitiativeSanofiAlzheimer's Association
KeywordsScannerNeuroimagingNuclear medicineComputer scienceGradient echoMagnetic resonance imagingMedicinePsychologyArtificial intelligenceRadiologyNeuroscience

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.040
metaresearch head score (Gemma)0.075
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: none
Teacher disagreement score0.040
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.075
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.074
GPT teacher head0.358
Teacher spread0.285 · 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

Citations68
Published2018
Admission routes2
Has abstractno

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