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Record W4411699964 · doi:10.1080/07853890.2025.2521433

Obesity: assessment and treatment across the care continuum

2025· review· en· W4411699964 on OpenAlexaff
Robert F. Kushner, Marla Shapiro

Bibliographic record

VenueAnnals of Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineObesityIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity is a chronic condition of dysregulated energy balance that is caused by a confluence of nutritional, neurological, hormonal and metabolic factors. Clinically, obesity is associated with myriad consequences to overall health. Treatment should be based on a shared decision-making process between the patient and their professional team that considers the stage of the disease, wellness goals and desired lifestyle and can include a combination of behavioural and lifestyle interventions, pharmacological therapies and surgery. The chronic nature of obesity necessitates adjustments in treatment plans to match the evolving needs and goals of the patient over time, establishing a 'care continuum.' METHODS: We conducted a broad, narrative literature search using PubMed for articles published on the assessment, diagnosis and treatment of adults with obesity. RESULTS: In this narrative literature review, we outline evidence-based best practices for the diagnosis and assessment of obesity along with the various available treatment modalities. We also present considerations for treating patients with obesity with a focus on the selection of obesity medications based on severity, concurrent conditions, mechanisms of action and treatment goals. Finally, we discuss lifestyle management, the shift from weight loss to weight maintenance, and the implications of these changes in the care continuum. CONCLUSION: Continued, individualized treatment of patients with obesity from diagnosis to weight maintenance is imperative for sustained weight reductions, and strategies should be tailored to the changing needs of patients over time.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Review
Teacher disagreement score0.971
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.136
GPT teacher head0.482
Teacher spread0.345 · 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 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

Citations4
Published2025
Admission routes1
Has abstractyes

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