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Record W1971271713 · doi:10.1017/s1461145710000167

Schizophrenia severity and clozapine treatment outcome association with oxytocinergic genes

2010· article· en· W1971271713 on OpenAlexafffund
Renan P. Souza, Vincenzo De Luca, Herbert Y. Meltzer, Jeffrey A. Lieberman, James L. Kennedy

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2010
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsClozapineSchizophrenia (object-oriented programming)Oxytocin receptorAntipsychoticPsychologyOxytocinPsychiatryMedicineInternal medicinePsychosisOncologyClinical psychology

Abstract

fetched live from OpenAlex

Antipsychotic drugs are the best means available for symptomatically treating individuals suffering from schizophrenia; however, there is a significant variability in clinical response to these psychotropic medications. Previous findings connect oxytocin (OXT) with schizophrenia and antipsychotic action. Therefore, we evaluated if OXT and OXT receptor (OXTR) genes might play a role in the symptom severity and clozapine treatment response in schizophrenia subjects. The rs2740204 variant in the OXT gene was significantly associated with treatment response (after 1000 permutations p=0.042) and nominally associated with negative symptoms in our sample. Furthermore, variants in the OXTR were nominally associated with severity of overall symptoms accessed using the Brief Psychiatric Rating Scale (rs237885, rs237887) as well as on the improvement of the positive symptoms (rs11706648, rs4686301, rs237899). Additional association studies in independent samples will be able evaluate whether OXT and OXTR genes are truly playing a role in the clozapine treatment outcome.

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)0.002
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.361
Teacher spread0.337 · 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

Citations72
Published2010
Admission routes2
Has abstractyes

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