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Record W2766569436 · doi:10.1097/nmd.0000000000000736

Exploring the Relationship Between Body Mass Index and Positive Symptom Severity in Persons at Clinical High Risk for Psychosis

2017· article· en· W2766569436 on OpenAlexaff
Fernando Caravaggio, Gary Brucato, Lawrence S. Kegeles, Eugénie Lehembre-Shiah, Leigh Y. Arndt, Tiziano Colibazzi, Ragy R. Girgis

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2017
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCollege of Physicians and Surgeons of Ontario
FundersNational Institute of Mental Health
KeywordsBody mass indexAntipsychoticPsychosisSchizophrenia (object-oriented programming)MedicineInternal medicinePsychiatryAntidepressant

Abstract

fetched live from OpenAlex

Metabolic health and positive symptom severity has been investigated in schizophrenia, but not in clinical high risk (CHR) patients. We hypothesized that greater body mass index (BMI) in CHR patients would be related to less positive symptoms. We examined this relationship in CHR patients being treated with 1) no psychotropic medications (n = 58), 2) an antipsychotic (n = 14), or 3) an antidepressant without an antipsychotic (n = 10). We found no relationship between BMI and positive symptoms in unmedicated CHR patients, the majority of whom had a narrow BMI range between 20 and 30. However, in the smaller sample of CHR patients taking an antidepressant or antipsychotic, BMI was negatively correlated with positive symptoms. Although potentially underpowered, these preliminary findings provide initial steps in elucidating the relationships between metabolic health, neurochemistry, and symptom severity in CHR patients.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.387
Teacher spread0.261 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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