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Record W2810942070 · doi:10.1002/smi.2819

The relation of atypical antipsychotic use and stress with weight in individuals at clinical high risk for psychosis

2018· article· en· W2810942070 on OpenAlexaff
Joya Hampton-Anderson, Hanan D. Trotman, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Tyrone D. Cannon, Barbara A. Cornblatt, Daniel H. Mathalon, Thomas H. McGlashan, Ming T. Tsuang, Diana O. Perkins, Larry J. Seidman, Scott W. Woods, Elaine F. Walker

Bibliographic record

VenueStress and Health · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Calgary
FundersNational Center for Advancing Translational SciencesNational Institute of Mental HealthCommonwealth of Massachusetts
KeywordsPsychosisAntipsychoticPsychologySchizophrenia (object-oriented programming)PsychiatryPsychosocialAntidepressantWeight gainAtypical antipsychoticWeight lossClinical psychologyMedicineInternal medicineBody weightObesityAnxiety

Abstract

fetched live from OpenAlex

Atypical antipsychotics (AT) and stress are related to weight gain in individuals with severe mental illness. This cross-sectional study examines AT use, stressful life events, and baseline weight in a sample of youth at clinical high risk for psychosis. Results showed that dependent and desirable life events moderated the relationship between AT use and weight after controlling for demographic factors and selective serotonin reuptake inhibitor antidepressant (AD) use. The relation of AD and weight was explored as a secondary analysis and showed no relation between AD use and weight. Further, stress did not moderate the relationship between AD medication and weight after controlling for antipsychotic use. Results suggest that stress exposure may exacerbate the relationship between ATs and increased weight in clinical high-risk populations. Findings have implications for the development of interventions to address psychosocial factors that worsen or buffer the adverse effects of antipsychotic medication on weight.

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 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 score0.022
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

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

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 head0.077
GPT teacher head0.415
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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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