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Record W2168301017 · doi:10.1017/s1461145712000314

Striatal glutamate and the conversion to psychosis: a prospective 1H-MRS imaging study

2012· article· en· W2168301017 on OpenAlexaff
Camilo de la Fuente‐Sandoval, Pablo León-Ortíz, Mariana Azcárraga, Rafael Favila, Sylvana Stephano, Ariel Graff‐Guerrero

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2012
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthInstituto Carlos Slim de la SaludConsejo Nacional de Ciencia y TecnologíaUniversity of California Institute for Mexico and the United StatesSistema Nacional de InvestigadoresEli Lilly and Company
KeywordsPsychosisStriatumGlutamate receptorPsychologyAbnormalityInternal medicineProton magnetic resonancePsychiatryMedicineAudiologyNeuroscienceNuclear magnetic resonanceDopamine

Abstract

fetched live from OpenAlex

Increased glutamate levels in the associative-striatum have been described in subjects at ultra-high risk for psychosis (UHR); nevertheless, it is unclear whether this abnormality predicts the conversion to psychosis. Nineteen subjects at UHR and 26 controls were studied using proton magnetic resonance spectroscopy. Subjects at UHR were clinically followed for 2 yr. Seven UHR subjects (37%) transitioned to a psychotic disorder and the remaining 12 did not exhibit psychotic symptoms at the most recent follow-up. The psychosis transition group had higher glutamate levels compared to both non-transition and control groups (p = 0.02 and p < 0.01, respectively; effect size 1.39). These pilot findings suggest that the conversion to psychosis is associated with increased glutamate levels in the associative-striatum.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.402
Teacher spread0.366 · 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

Citations87
Published2012
Admission routes1
Has abstractyes

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