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Record W1968446465 · doi:10.1007/s12170-013-0321-1

Management of Medication-Related Cardiometabolic Risk in Patients with Severe Mental Illness

2013· article· en· W1968446465 on OpenAlexaff
Donna J. Lang, Alasdair M. Barr, Ric M. Procyshyn

Bibliographic record

VenueCurrent Cardiovascular Risk Reports · 2013
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsBC Children's HospitalBC Mental Health & Substance Use ServicesUniversity of British Columbia
Fundersnot available
KeywordsMedicineAntipsychoticSchizophrenia (object-oriented programming)ManiaBipolar disorderMental illnessPsychiatryPsychological interventionMetabolic syndromeDepression (economics)Intensive care medicineInternal medicineMental healthObesityLithium (medication)

Abstract

fetched live from OpenAlex

Severe psychotic disorders, which on their own may be a risk factor for metabolic disorder and cardiovascular illness, are clinically compounded by the significant adverse side effects of antipsychotic medications. The majority of patients with severe psychotic disorders (i.e., schizophrenia, bipolar disorder, mania, and depression) must take antipsychotic medications to treat their psychoses and, subsequently, will require efficacious interventions to manage the metabolic consequences of pharmacologic treatment to mitigate excessive mortality associated with cardiovascular illness. We have reviewed the metabolic consequences of antipsychotic treatment and discussed pilot findings from a new nonpharmacologic intervention study looking at the clinical benefits of regular exercise as a management tool for the cardiometabolic risk factors in a cohort with severe mental illness.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 designNot applicable
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

Citations11
Published2013
Admission routes1
Has abstractyes

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