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Record W4416753414 · doi:10.1101/2025.11.25.25339898

Charting Brain Structure in 22q11.2 Deletion Syndrome with Clinical Neuroimaging

2025· preprint· en· W4416753414 on OpenAlexaff
Benjamin Jung, J. Eric Schmitt, Jakob Seidlitz, Jenna Schabdach, T. Blaine Crowley, Lena Dorfschmidt, Ayan S. Mandal, Dabriel Zimmerman, Remo Williams, Smrithi Prem, Elizabeth Levitis, Margaret Gardner, K. Cyr, Viveknarayanan Padmanabhan, Jerome H. Taylor, Kosha Ruparel, Rune Bøen, Carrie E. Bearden, Christopher R. K. Ching, Bogdan Paşaniuc, Stewart A. Anderson, Daniel E. McGinn, Elaine H. Zackai, Beverly S. Emanuel, Sarah Hopkins, Madeline Chadehumbe, Karen Low, Tim Cole, Richard A. I. Bethlehem, Russell T. Shinohara, J. William Gaynor, David R. Roalf, Raquel E. Gur, Donna M. McDonald‐McGinn, Aaron Alexander‐Bloch

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersNational Institute of Mental Health
KeywordsNeuroimagingCognitionBrain Structure and FunctionImaging geneticsDeletion syndromeBrain morphometryValue (mathematics)

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.006
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.024
GPT teacher head0.351
Teacher spread0.327 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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