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Record W2995641799 · doi:10.1038/s41386-019-0589-z

Levels of glutamatergic neurometabolites in patients with severe treatment-resistant schizophrenia: a proton magnetic resonance spectroscopy study

2019· article· en· W2995641799 on OpenAlexaff
Ryosuke Tarumi, Sakiko Tsugawa, Yoshihiro Noda, Eric Plitman, Shiori Honda, Karin Matsushita, Sofia Chavez, Kyosuke Sawada, Masataka Wada, Mié Matsui, Shinya Fujii, Takahiro Miyazaki, M. Mallar Chakravarty, Hiroyuki Uchida, Gary Remington, Ariel Graff‐Guerrero, Masaru Mimura, Shinichiro Nakajima

Bibliographic record

VenueNeuropsychopharmacology · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthMcGill UniversityDouglas Mental Health University Institute
FundersJapan Society for the Promotion of ScienceNOVARTIS Foundation (Japan) for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyUehara Memorial FoundationTakeda Science Foundation
KeywordsAnterior cingulate cortexSchizophrenia (object-oriented programming)Internal medicineGlutamatergicPositive and Negative Syndrome ScaleMedicineAntipsychoticPopulationPathophysiologyGlutamineGlutamate receptorPsychiatryPsychosisPsychologyGastroenterologyEndocrinologyReceptorChemistry

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.283
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.297
Teacher spread0.287 · 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 teacher head, 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

Citations63
Published2019
Admission routes1
Has abstractno

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