Eligibility for Obesity Treatment and Risk of Mortality in Men
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk of all-cause and cardiovascular disease (CVD) mortality associated with each outcome of the NIH obesity treatment algorithm and to examine the effects of cardiorespiratory fitness on the risk of mortality associated with these outcomes. RESEARCH METHODS AND PROCEDURES: The NIH obesity treatment algorithm was applied to 18,666 men (20 to 64 years of age) from the Aerobics Center Longitudinal Study in Dallas, TX, examined between 1979 and 1995. Risk of all-cause and CVD mortality was assessed using Cox proportional hazards regression. RESULTS: A total of 7029 men (37.7%) met the criteria for needing weight loss treatment [overweight (BMI = 25 to 29.9 kg/m2 or WC > 102 cm) with > or =2 CVD risk factors or obese (BMI > or = 30 kg/m2)]. Mortality surveillance through 1996 identified 435 deaths (151 from CVD) during 191,364 man-years of follow-up. Compared with the normal weight reference group, the hazard ratios (95% confidence interval) for death from all causes were 0.63 (0.45 to 0.88), 1.23 (0.98 to 1.54), 1.05 (0.60 to 1.85), and 1.71 (1.64 to 2.31) for men who were overweight with <2 CVD risk factors, overweight with > or = 2 CVD risk factors, obese with <2 CVD risk factors, and obese with > or =2 CVD risk factors, respectively. Corresponding hazard ratios for CVD mortality were 0.72 (0.38 to 1.37), 1.67 (1.12 to 2.50), 1.69 (0.67 to 4.30), and 3.31 (2.07 to 5.30). Including physical fitness as a covariate significantly attenuated all risk estimates. DISCUSSION: The NIH obesity treatment algorithm is useful in identifying men at increased risk of premature mortality; however, including an assessment of fitness would help improve risk stratification among all groups of patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".