Residential Greenness and Long-term Mortality Among Patients Who Underwent Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
BACKGROUND: Studies have reported inverse associations between exposure to residential greenness and mortality. Greenness has also been associated with better surgical recovery. However, studies have had small sample sizes and have been restricted to clinical settings. We investigated the association between exposure to residential greenness and all-cause mortality among a cohort of cardiac patients who underwent coronary artery bypass graft (CABG) surgery. METHODS: We studied this cohort of 3,128 CABG patients between 2004 and 2009 at seven cardiothoracic departments in Israel and followed patients until death or 1st May 2021. We collected covariate information at the time of surgery and calculated the patient-level average normalized difference vegetation index (NDVI) over the entire follow-up in a 300 m buffer from the home address. We used Cox proportional hazards regression models to estimate associations between greenness and death, adjusting for age, sex, origin, socioeconomic status, type of hospital admission, peripherality, air pollution, and distance from the sea. RESULTS: Mean age at surgery was 63.8 ± 10.6 for men and 69.5 ± 10.0 for women. During an average of 12.1 years of follow-up (37,912 person-years), 1,442 (46%) patients died. A fully adjusted Cox proportional hazards model estimated a 7% lower risk of mortality (HR: 0.93, 95% CI = [0.85, 1.00]) per 1 interquartile range width increase (0.04) in NDVI. Results were robust to the use of different buffer sizes (100 m-1,250 m from the home) and to the use of average NDVI exposure during the first versus the last 2 years of follow-up. CONCLUSIONS: Residential greenness was associated with lower risk of mortality in CABG patients.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".