Complementary Role of BMI and EOSS in Predicting All-Cause and Cause-Specific Mortality in People with Overweight and Obesity
Bibliographic record
Abstract
Objective: To assess the complementary role of the Body Mass Index (BMI) and Edmonton Obesity Staging System (EOSS) in predicting all-cause and cause-specific mortality in people living with overweight and obesity (PLwOW/O). Methods: A longitudinal analysis of prospectively collected data from the 1999–2018 cycles of the National Health and Nutrition Examination Survey (NHANES) was conducted. The association between BMI, EOSS, and mortality was evaluated through Cox regression models, adjusted for confounders. Results: The analysis included 36,529 subjects; 5329 deaths occurred over a median follow-up of 9.1 years (range: 0–20.8). An increased mortality risk was observed for obesity class II and III (HR = 1.21, 95% CI 1.08–1.36, p = 0.001 and HR = 1.58, 95% CI 1.39–1.80, p < 0.001; compared to overweight), and for EOSS stage 2 and 3 (HR = 1.36, 95% CI 1.16–1.58, p < 0.001 and HR = 2.66, 95% CI 2.26–3.14, p < 0.001; compared to stage 0/1). The prognostic role of BMI was more pronounced in younger patients, males, and non-Black individuals, while that of EOSS was stronger in women. Both BMI and EOSS independently predicted cardiovascular- and diabetes-related mortality. EOSS stage 3 was the only predictor of death from malignancy or renal causes. Conclusions: BMI and EOSS independently predict all-cause and cause-specific mortality in PLwOW/O. Their integrated use seems advisable to best define the obesity-related mortality risk.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".