Diagnosis and Treatment of Obesity in Adults: An Applied Evidence-Based Review
Bibliographic record
Abstract
BACKGROUND: Obesity is epidemic and leads to substantial morbidity/mortality. Effective strategies exist for managing obesity yet are rarely used by physicians. This applied evidence-based review provides a rationale for the diagnosis and treatment of obesity in adults by providing test characteristics for the body mass index (BMI) and number needed to treat (NNT) for relevant treatments. METHODS: We integrated evidence supporting recommendations from scientific bodies addressing obesity in adults, including: the National Heart, Lung, and Blood Institute, the World Health Organization, the Canadian Task Force on Preventive Health Care, and the US Preventive Task Force. In addition, pertinent studies were identified from MEDLINE, Database of Abstracts of Reviews of Effectiveness, and the Cochrane Database. RESULTS: (1) manage obesity as a chronic relapsing disease; (2) use BMI as a vital sign to screen for overweight/obese patients and to decide treatment (positive predictive value of 97%); (3) modest weight loss (10%) positively affects prevention/treatment of hypertension (NNT = 3), diabetes (NNT = 9), and hyperlipidemia; (4) effective treatments exist for overweight/obese patients and a combination of diet and exercise provides the best results (NNT = 7); (5) counsel patients to achieve a goal of 10% reduction in weight (500 to 800 kcal/day decrease to affect 1- to 2-pound loss/week); (6) counsel patients to exercise to achieve a goal of any increased energy expenditure. CONCLUSIONS: Weight loss has an impact on important disease states and risk factors. Effective strategies exist for the management of obesity when viewed as a chronic relapsing disease.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".