Obesity: assessment and treatment across the care continuum
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".