Incidence and associated risk factors of cancer in patients after infective endocarditis hospitalization: A propensity score matched analysis
Bibliographic record
Abstract
BACKGROUND: The relationship between infective endocarditis (IE) and malignancies had been reported. However, cancer development is multifactorial and mortality is high in IE survivors. We aimed to estimate the absolute cancer risk in IE survivors and tried to find the potential risk factors. METHODS: This nationwide, population-based cohort study evaluated 8649 newly diagnosed IE who survived after discharge from first hospitalization using the Taiwan National Health Insurance Research Data-base (NHIRD). Propensity score method was used at a 1:1 ratio based on age, gender, income, urbanization level, Charlson comorbidity index (CCI), concomitant medication, and medical history. The primary outcomes were all specific cancer types. All the variables matched for propensity score were analyzed to find their association with cancer occurrence. RESULTS: Compared with the matched cohort, IE survivors increase the risk of cancer (adjusted hazard ratio [aHR], 1.64; 95% confidence interval [CI], 1.39- 1.94), as well as significantly elevated risks of digestive (aHR, 2.27; 95% CI, 1.76- 2.92) and hematologic (aHR, 2.73; 95% CI, 2.31-5.71) malignancies. Age (aHR, 1.47; 95% CI, 1.36-1.57), male (aHR, 1.49; 95% CI, 1.18-1.90), CCI score (aHR, 1.05; 95% CI, 1.01-1.10) are risk factors for cancer occurrence among IE survivors, while aspirin use reduced the cancer risk (aHR, 0.73; 95% CI, 0.54-0.98). CONCLUSION: Our study provides further investigation between IE and cancer risk. In conclusion, cancer risk, particularly digestive and hematologic malignancies, is substantially increased in IE survivors. Our results also provide additional evidence that aspirin use is effective in reducing cancer risk in IE survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".