Diagnosis and Treatment of Obesity in Adults: An Applied Evidence-Based Review
Why this work is in the frame
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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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it