MétaCan
Menu
Back to cohort
Record W4234396238 · doi:10.1093/schbul/sbm022

Interventions to Reduce Weight Gain in Schizophrenia

2007· article· en· W4234396238 on OpenAlexaff
Guy Faulkner, T. Cohn, Gary Remington

Bibliographic record

VenueSchizophrenia Bulletin · 2007
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Weight gainPsychological interventionPsychologyPsychiatryMedicineBody weightInternal medicine

Abstract

fetched live from OpenAlex

Obesity is a common problem for individuals with schizophrenia, a problem that has been exacerbated more recently with the increased use of second-generation antipsychotics, many of which are associated with the risk of weight gain and metabolic disturbance. Recently, a Cochrane review has been published examining pharmacological and nonpharmacological (diet/exercise) interventions for reducing and/or preventing weight gain in this population. The objective of the review was to determine the effects of both pharmacological (excluding medication switching) and nonpharmacological strategies for reducing or preventing weight gain in individuals with schizophrenia. We searched key databases and the Cochrane Schizophrenia Group's trials register and reference sections within relevant articles, hand searched key journals, and contacted the first author of each relevant study and other experts to collect further information. All clinical randomized controlled trials (RCTs) comparing any pharmacological or nonpharmacological intervention for weight gain (eg, diet and exercise counseling including elements of cognitive and/or behavioral modification) with standard care or other treatments for individuals with schizophrenia or schizophrenia-like illnesses were selected.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.022
GPT teacher head0.304
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations92
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueSchizophrenia BulletinSame topicDiet and metabolism studiesFrench-language works237,207