Life’s Essential 8 Trajectories and Risk of Stroke: A Prospective Cohort Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Evidence is lacking regarding long-term patterns of change in Life’s Essential 8 (LE8) and their association with the risk of stroke. We aim to evaluate LE8 trajectories and examine their association with the risk of stroke in China. METHODS: This study, conducted in a workplace setting, recruited 26 719 participants (average age, 46.02±11.27 years and a male population of 73.73%) who had no history of stroke and consecutively participated in 6 surveys from 2006 to 2016. Repeated LE8 measurements were determined by taking the unweighted average of the 8 component scores ranging from 0 to 100. People with higher scores had better overall cardiovascular health. By examining the medical records of the participants, stroke cases were identified for the period from 2016 to 2020. A latent mixture model was applied to classify the trajectory clusters of LE8 from 2006 to 2016, and Cox proportional hazard models were used to analyze the data. RESULTS: Five LE8 trajectories were detected between 2006 and 2016. Four hundred ninety-eight incident strokes including 55 (11.04%) hemorrhagic and 458 (91.97%) ischemic strokes were documented. After adjusting for covariates, the hazard ratios and 95% CIs for the association between stable-low, moderate-increasing, moderate-stable, and high-stable trajectories and incident stroke, compared with the moderate-decreasing trajectory, were 1.42 (1.11–1.84), 0.73 (0.56–0.96), 0.49 (0.39–0.62), and 0.19 (0.11–0.32), respectively. Individuals with high LE8 status (LE8≥80) exhibited a significantly reduced risk of stroke compared with those with low one (LE8≤49; P -trend <0.001). A faster annual growth in LE8 was related to a lower risk of stroke. CONCLUSIONS: Maintaining high LE8 over an extended period and high baseline LE8 status were related to a decreased risk of stroke. Despite the initial low level of LE8, improvement in LE8 attenuates or even reverses the risk of stroke.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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