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Record W2963428142 · doi:10.1016/j.schres.2019.07.020

Resting-state functional connectivity in treatment response and resistance in schizophrenia: A systematic review

2019· review· en· W2963428142 on OpenAlexafffund
Nathan Chan, Julia Kim, Parita Shah, Eric E. Brown, Eric Plitman, Fernando Carravaggio, Yusuke Iwata, Philip Gerretsen, Ariel Graff‐Guerrero

Bibliographic record

VenueSchizophrenia Research · 2019
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMental Health Research CanadaUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchOffice of Graduate Student Assistantships and Fellowships, George Washington UniversityJapan Foundation for Aging and HealthW. Garfield Weston FoundationUniversity of TorontoNational Alliance for Research on Schizophrenia and DepressionMitsukoshi Health and Welfare FoundationOntario Mental Health FoundationNational Institutes of HealthInstituto de Ciencia y Tecnología del Distrito FederalConsejo Nacional de Ciencia y TecnologíaCentre for Addiction and Mental Health
KeywordsPsycINFOSchizophrenia (object-oriented programming)Functional magnetic resonance imagingContext (archaeology)MEDLINEMeta-analysisPsychologyMedicineClinical psychologyNeurosciencePsychiatryInternal medicineBiology

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.189
GPT teacher head0.399
Teacher spread0.209 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations28
Published2019
Admission routes2
Has abstractno

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