Multimorbidity and the Risk of Sudden Cardiac Death
Bibliographic record
Abstract
OBJECTIVE: To assess the prospective associations of multimorbidity status and level with the risk of sudden cardiac death (SCD). METHODS: Multimorbidity was defined as the presence of at least two multiple long-term conditions (hypertension, cardiovascular disease, type 2 diabetes, chronic kidney disease, chronic bronchitis, and other chronic lung conditions) among 2598 men 42 to 61 years of age who were recruited into the KIHD (Kuopio Ischemic Heart Disease) study from March 1, 1984, to December 31, 1989. Hazard ratios with 95% CIs were estimated. RESULTS: During a median follow-up of 27.8 years, 296 SCDs were recorded. In analysis adjusted for several established cardiovascular risk factors including socioeconomic and lifestyle characteristics, the HR (95% CI) for SCD comparing men with multimorbidity vs no multimorbidity was 1.97 (95% CI, 1.54 to 2.51). Compared with men with no multimorbidity, the corresponding adjusted HRs (95% CIs) for SCD were 1.93 (95% CI, 1.50 to 2.47) for men with two to three conditions and 2.66 (95% CI, 1.34 to 5.28) for men with four to five conditions. CONCLUSION: In middle-aged and older men, multimorbidity is strongly linked to an increased risk of SCD, independent of known cardiovascular risk factors. Furthermore, the risk of SCD rises progressively with the number of coexisting health conditions.
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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 | 0.001 | 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) | 0.001 | 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".