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Record W4411661858 · doi:10.1007/s00415-025-13201-1

Regional free-water diffusion is more strongly related to neuroinflammation than neurodegeneration

2025· article· en· W4411661858 on OpenAlexaff
Vishaal Sumra, Mohsen Hadian, Allison A. Dilliott, Sali M.K. Farhan, Andrew Frank, Anthony E. Lang, Angela Roberts, Angela K. Troyer, Stephen R. Arnott, Connie Marras, David F. Tang‐Wai, Elizabeth Finger, Ekaterina Rogaeva, J. B. Orange, Joel Ramı́rez-Emiliano, Lorne Zinman, Malcolm A. Binns, Michael Borrie, Morris Freedman, Miracle Ozzoude, Robert Bartha, Richard H. Swartz, David G. Muñoz, Mario Masellis, Sandra E. Black, Roger A. Dixon, Dar Dowlatshahi, David A. Grimes, Ayman Hassan, Robert A. Hegele, Sanjeev Kumar, Stephen Pasternak, Bruce G. Pollock, Tarek K. Rajji, Demetrios J. Sahlas, Gustavo Saposnik, Maria Carmela Tartaglia

Bibliographic record

VenueJournal of Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMcMaster UniversityNOSM UniversityWomen and Children’s Health Research InstituteUniversity of AlbertaCentre for Addiction and Mental HealthToronto Public HealthSunnybrook Health Science CentreWestern UniversityBruyèreOttawa HospitalHealth Sciences CentreUniversity Health NetworkWilfrid Laurier UniversityMontreal Neurological Institute and HospitalBaycrest HospitalMcGill UniversityUniversity of OttawaOntario Brain InstituteToronto Western HospitalCanada Research Chairs
Fundersnot available
KeywordsNeuroinflammationNeurodegenerationNeurologyNeuroradiologyNeuroscienceDiffusion MRIMedicinePsychologyMagnetic resonance imagingPathologyInternal medicineInflammationDisease

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.333
Teacher spread0.302 · 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

Citations9
Published2025
Admission routes1
Has abstractno

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