Comparing the Ability of Two Comprehensive Clinical Staging Systems to Predict Mortality: EOSS and CMDS
Bibliographic record
Abstract
Objective Differences in discriminative and predictive ability for all‐cause mortality of two clinical staging systems, the Edmonton Obesity Staging System (EOSS) and Cardiometabolic Disease Staging (CMDS), were estimated. Methods Data for nonpregnant persons aged 40 to 75 years were extracted from the National Health and Nutrition Examination Survey. Predictive and discriminative ability was assessed using pseudo‐R2 and C‐statistics. Median years of life lost were also computed for each score. Results Differences in out‐of‐sample estimates of pseudo‐R2 and C‐statistics (EOSS model as reference) were 0.02 (95% CI: 0.01‐0.04) (Kent pseudo‐R2), 0.03 (0.01‐0.04) (Royston pseudo‐R2), and 0.02 (0.01‐0.02) (C‐statistics). The median years of life lost for EOSS scores 2 and 3 (low to high risk) for a reference person were 1.19 and 6.76 years. Those for CMDS scores 1, 2, 3, and 4 (low to high risk) were 1.53, 2.90, 3.91, and 8.51 years. Consistent results from the in‐sample estimates were observed. Conclusions CMDS had statistically significantly greater predictive and discriminative ability than EOSS for persons aged 40 to 75. While the clinical relevance of these differences is unknown, CMDS may have greater clinical utility given that it uses fewer items to risk stratify. The clinical relevance and utility need to be studied further.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".