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Record W2140756130 · doi:10.4103/0973-1229.58819

Weight-Gain in Psychiatric Treatment: Risks, Implications, and Strategies for Prevention and Management

2010· article· en· W2140756130 on OpenAlexaff
Amresh Shrivastava, MeganE Johnston

Bibliographic record

VenueMens Sana monographs · 2010
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of TorontoLawson Health Research InstituteWestern University
Fundersnot available
KeywordsWeight gainPsychological interventionPsychiatryPsychopharmacologyPsychologyMedicineClinical PracticePsychotherapistClinical psychologyBody weightFamily medicine

Abstract

fetched live from OpenAlex

Weight-gain in psychiatric populations is a common clinical challenge. Many patients suffering from mental disorders, when exposed to psychotropic medications, gain significant weight with or without other side-effects. In addition to reducing the patients' willingness to comply with treatment, this weight-gain may create added psychological or physiological problems that need to be addressed. Thus, it is critical that clinicians take precautions to monitor and control weight-gain and take into account and treat all problems facing an individual. In this review, we examine some of the key issues surrounding weight-gain in individuals suffering from mental disorders for contemporary practitioners in community clinics. We describe some factors known to make certain patients more susceptible to treatment-induced weight-gain and mechanisms implicated in this process. We also highlight a few psychological and pharmacological interventions that have proven effective in weight management. Importantly, we provide critical steps for management and prevention of weight-gain and related issues in the clinical practice of psychopharmacology.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.167
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.033
GPT teacher head0.356
Teacher spread0.322 · 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 teacher head, 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

Citations49
Published2010
Admission routes1
Has abstractyes

Explore more

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