EFFECTS OF LOW NEIGHBOURHOOD SOCIOECONOMIC STATUS ON INFLAMMATION, OXIDATIVE STRESS, AND RISK OF MORTALITY IN PATIENTS WITH CORONARY ARTERY DISEASE
Bibliographic record
Abstract
Background: Socioeconomicstatus (SES) is an influential determinant of prognosis in coronary artery disease (CAD). Patient neighbourhood SES may contribute to CAD outcomes, beyond effects of personal SES. Methods: Following 485 CAD patients for > 10 years, we examine the effect of neighbourhood income, education, and unemployment on survival, and investigate relationships between SES and markers of inflammation and oxidative stress. Results: SES was associated significantly with risk of mortality, however this relationship was not observed for cardiovascular death. Each one quintile decrease in income, education, and employment was associated with a 32%, 40% and 45% greater risk of non-cardiovascular mortality, respectively. Inflammatory and oxidative stress markers correlate with income, but do not diminish associations between neighbourhood SES and mortality. Conclusions: Significant disparities in non-cardiovascular mortality related to neighbourhood SES were observed in this study, which argues for greater attention to socioeconomic factors in chronic disease prevention and health care delivery. C.L.H. is supported bya Providence Health Research Institute & Canadian Institutes of HealthResearch MD/PhD Studentship Award, and a Michael Smith Foundation for HealthResearch Trainee Award. (colour figure available in PDF version)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".