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Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity

2023· article· en· W4379162608 on OpenAlexafffund
Tulip Marawi, Peter Zhukovsky, Neda Rashidi‐Ranjbar, Christopher R. Bowie, Heather Brooks, Corinne E. Fischer, Alastair J. Flint, Nathan Herrmann, Linda Mah, Bruce G. Pollock, Tarek K. Rajji, Maria Carmela Tartaglia, Aristotle N. Voineskos, Benoit H. Mulsant, Lillian Lourenço, Daniel M. Blumberger, Meryl A. Butters, Damien Gallagher, Angela Golas, Ariel Graff, James L. Kennedy, Krista L. Lanctôt, Sanjeev Kumar, Shima Ovaysikia, Mark Rapoport, Kevin E. Thorpe, N. P. L. G. Verhoeff

Bibliographic record

VenueBiological Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalOccupational Cancer Research CentreHealth Sciences CentreSunnybrook Health Science CentreToronto Dementia Research AllianceUniversity of TorontoUniversity Health NetworkQueen's UniversitySt. Michael's HospitalCentre for Addiction and Mental Health
FundersNational Institute on AgingW. Garfield Weston FoundationHealth CanadaLinda C. Campbell FoundationUniversity of TorontoNational Institutes of HealthAlzheimer SocietyCanada Foundation for InnovationOntario Ministry of Research, Innovation and ScienceCanada Research ChairsOntario Ministry of Health and Long-Term CareMinistério da Ciência, Tecnologia e InovaçãoBrightFocus FoundationGenome CanadaCanadian Institutes of Health ResearchAlzheimer's Drug Discovery FoundationWeston Brain InstituteCentre for Addiction and Mental Health FoundationFondation Brain CanadaNational Institute of Mental HealthPatient-Centered Outcomes Research InstituteCanadian Foundation for Healthcare ImprovementOntario Brain InstituteAlzheimer's AssociationBrain and Behavior Research Foundation
KeywordsGray (unit)CognitionWhite matterMultivariate statisticsMultivariate analysisPsychologyCognitive impairmentClinical psychologyMajor depressive disorderPsychiatryMedicineInternal medicineMagnetic resonance imaging

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)0.001
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.324
Teacher spread0.303 · 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

Citations25
Published2023
Admission routes2
Has abstractno

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