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Record W4353055421 · doi:10.1038/s44220-023-00024-0

Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia

2023· article· en· W4353055421 on OpenAlexafffund
Yuchao Jiang, Jijun Wang, Enpeng Zhou, Lena Palaniyappan, Cheng Luo, Gong‐Jun Ji, Jie Yang, Yingchan Wang, Yuyanan Zhang, Chu‐Chung Huang, Shih‐Jen Tsai, Xiao Chang, Chao Xie, Wei Zhang, Jinchao Lv, Di Chen, Chun Shen, Xinran Wu, Bei Zhang, Nanyu Kuang, Yun-Jun Sun, Jujiao Kang, Jie Zhang, Huan Huang, Hui He, Mingjun Duan, Yingying Tang, Tianhong Zhang, Chunbo Li, Xin Yu, Tianmei Si, Weihua Yue, Zhening Liu, Long‐Biao Cui, Kai Wang, Jingliang Cheng, Ching‐Po Lin, Dezhong Yao, Wei Cheng, Jianfeng Feng

Bibliographic record

VenueNature Mental Health · 2023
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteLawson Health Research InstituteWestern University
FundersNational Institutes of HealthHigher Education Discipline Innovation ProjectFonds de Recherche du Québec - SantéShanghai Rising-Star ProgramMcGill UniversityScience and Technology Commission of Shanghai MunicipalityNational Center for Research ResourcesNational Institute of Mental HealthChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsTranscranial magnetic stimulationNeuroimagingAtrophySchizophrenia (object-oriented programming)NeurosciencePsychologyMagnetic resonance imagingAlzheimer's Disease Neuroimaging InitiativeDiseaseNeurophysiologyMedicinePsychiatryPathologyStimulationCognitionRadiologyCognitive impairment

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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

Citations94
Published2023
Admission routes2
Has abstractno

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