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Record W4387077880 · doi:10.1007/s11920-023-01458-0

Antipsychotic-Induced Weight Gain in Severe Mental Illness: Risk Factors and Special Considerations

2023· review· en· W4387077880 on OpenAlex
Nicolette Stogios, Bailey Humber, Sri Mahavir Agarwal, Margaret Hahn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Psychiatry Reports · 2023
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsPrincess Margaret Cancer CentreDiabetes CanadaUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineAntipsychoticPsychological interventionPsychiatryScope (computer science)Weight gainClinical PracticeSchizophrenia (object-oriented programming)MEDLINEIntensive care medicineFamily medicineBody weightInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Weight gain is a disconcerting issue experienced by patients treated with antipsychotics (APs). This review summarizes current knowledge on the prevalence, etiology, and risk factors for antipsychotic-induced weight gain (AIWG), and evidence for interventions, including special considerations. RECENT FINDINGS: Predisposing risk factors for AIWG include lack of prior AP exposure, sex, and age. AP dose and duration of exposure are additional treatment-related factors that may contribute to this issue. Among current approaches to target AIWG, metformin has the most evidence to support its use, and this is increasingly reflected in clinical guidelines. While lifestyle approaches are recommended, cost-effectiveness and scalability represent limitations. More research is needed to identify newer treatment options and inform clinical recommendations for AIWG. Concerns around scope of practice in psychiatry to address AIWG and related comorbidities will require enhanced training opportunities and interdisciplinary collaborations, as well as updated position statements/practice guidelines emphasizing prevention.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
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)0.0000.000

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 head0.086
GPT teacher head0.390
Teacher spread0.304 · 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