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Record W4362456799 · doi:10.1093/schbul/sbad037

Pharmacological Interventions for the Prevention of Antipsychotic-Induced Weight Gain in People With Schizophrenia: A Cochrane Systematic Review and Meta-Analysis

2023· review· en· W4362456799 on OpenAlexaff
Sri Mahavir Agarwal, Nicolette Stogios, Guy Faulkner, Margaret Hahn

Bibliographic record

VenueSchizophrenia Bulletin · 2023
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of British ColumbiaDiabetes CanadaUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsWeight gainSchizophrenia (object-oriented programming)AntipsychoticMedicineMeta-analysisAdverse effectPsychological interventionLife expectancyDiseaseObesityPsychiatryPopulationIntensive care medicineInternal medicineBody weightEnvironmental health

Abstract

fetched live from OpenAlex

Patients with schizophrenia are burdened by higher rates of obesity, cardiovascular disease and reduced life expectancy than the general population. In addition to illness, genetic and lifestyle factors, the associated weight gain and metabolic adverse effects of antipsychotic (AP) medications are known to exacerbate and accelerate these cardiometabolic problems significantly. Given the detrimental consequences of weight gain and other metabolic disturbances, there is an urgent need for safe and effective strategies to manage these issues as early on as possible. This review summarizes the literature of adjunctive pharmacological interventions aimed at preventing AP-induced weight gain.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.014
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.141
GPT teacher head0.426
Teacher spread0.285 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations13
Published2023
Admission routes1
Has abstractyes

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