Additional file 1 of The impact of cardiovascular health and frailty on mortality for males and females across the life course
Bibliographic record
Abstract
Additional file 1. Expanded Methods. Additional details for assessments of cardiovascular health behaviours and factors. Expanded Results. Additional results for individual LS7 metrics and non-CVD mortality. Expanded Discussion. Additional discussion for non-CVD mortality. Table S1. 33-item frailty index. Table S2. Cardiovascular health behaviors and factors by tertiles of Life’s Simple 7 score and sex. Table S3. Combined effect of frailty and cardiovascular health on mortality in females. Table S4. Combined effect of FI and LS7 on all-cause mortality in males across ages. Table S5. Combined effect of FI and LS7 on CVD mortality in males across ages. Table S6. Association of frailty and cardiovascular health with mortality in females without a CVD diagnosis. Table S7. Association of frailty and cardiovascular health with mortality in males without a CVD diagnosis at ages 30, 50, and 70. Table S8. Characteristics of participants excluded due to incomplete cardiovascular information. Table S9. Mortality rates by frailty and Life’s Simple 7 score groups in males. Table S10. Association of frailty and cardiovascular health with non-CVD mortality in females. Table S11. Associations of frailty and cardiovascular health with non-CVD mortality in males at ages 30, 50, and 70. Table S12. Combined effect of FI and LS7 on non-CVD mortality in males across ages. Table S13. Demographic statistics of all males and females by age groups. Table S14. Cardiovascular health behaviors and factors by age groups. Figure S1. Proportion of participants in each Life’s Simple 7 score tertile by frailty index level (33-item version) for males and females. Figure S2. Simple slopes of the association between Life’s Simple 7 score and the 33-item frailty index from a linear regression model for males and females. Figure S3. Cox regression and Fine-Gray models for combined effect of Life’s Simple 7 score and frailty on all-cause and CVD-related mortality in females without a CVD diagnosis. Figure S4. Cox regression and Fine-Gray models for combined effect of Life’s Simple 7 score and frailty on all-cause and CVD-related mortality in males without a CVD diagnosis, with age centered at 30, 50, and 70. Figure S5. Multiple linear regression model for the association between individual cardiovascular health metrics and frailty.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.873 | 0.141 |
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".