Risk of malignancy long after acute coronary syndrome in selected urban and rural areas and comparison with smoking risk: the ABC-7* study on Heart Disease
Bibliographic record
Abstract
BACKGROUND: Increased cancer risk has been reported in patients with acute coronary syndrome (ACS). OBJECTIVES: To investigate geographic differences in risk malignancy long after ACS. METHODS: We enrolled 586 ACS patients admitted to hospitals in three provinces in the Veneto region of Italy in this prospective study. Patient's residency was classified into three urban and three nearby rural areas. RESULTS: All (except for 3) patients completed the follow-up (22 years or death) and 54 % were living in rural areas. Sixteen patients had pre-existing malignancy, and 106 developed the disease during follow-up. Cancer prevalence was 17 % and 24 % (p = 0.05) and incidence of malignancy was 16 and 21/1000 person-years for urban and rural areas, respectively. In unadjusted logistic regression analysis, cancer risk increased from urban to rural areas (odds ratio [OR] 3.4;95 % confidence interval [CI] 1.7-7.1; p = 0.001), with little change from north to south provinces (OR 1.5;95 % CI 1.0-2.2; p = 0.06). Yet, we found a strong positive interaction between urban-rural areas and provinces (OR 2.1;95 % CI 1.2-3.5; p = 0.003). These results kept true in the fully adjusted model. Unadjusted Cox regression analysis revealed increasing hazards ratios (HRs) for malignancy onset from urban to rural areas (HR 3.0;95 % CI 1.5-6.2; p = 0.02), but not among provinces (HR 1.3;95 % CI 1.0-2.0; p = 0.14). Also, we found a strong positive interaction between geographic areas (HR 2.1;95 % CI 1.3-3.5; p = 0.002), even with a fully adjusted model. CONCLUSIONS: The results in unselected real-world patients demonstrate a significant geographic difference in malignancy risk in ACS patients, with the highest risk in the north-rural area.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".