Age Effect on Incidence, Physical, and Psychiatric Comorbidity for Sudden Cardiac Death in Schizophrenia: Effet de l’âge sur l’incidence, la comorbidité physique et psychiatrique de la mort cardiaque subite dans la schizophrénie
Bibliographic record
Abstract
OBJECTIVE: The pathogenesis of sudden cardiac death may differ between younger and older adults in schizophrenia, but evidence remains scant. This study investigated the age effect on the incidence and risk of the physical and psychiatric comorbidity for sudden cardiac death. METHODS: Using 2000 to 2016 data from the Taiwan National Health Insurance Research Database and Department of Health Death Certification System, we identified a national cohort of 170,322 patients with schizophrenia, 1,836 of whom had a sudden cardiac death. Standardized mortality ratios (SMRs) were estimated. Hazard ratios and population attributable fractions of distinctive comorbidities for sudden cardiac death were assessed. RESULTS: The SMRs of sudden cardiac death were all >1.00 across each age group for both sexes, with the highest SMR in male patients aged <35 years (30.88, 95% CI: 26.18-36.18). The fractions of sudden cardiac death attributable to hypertension and congestive heart failure noticeably increased with age. By contrast, the fraction attributable to drug-induced mental disorder decreased with age. Additionally, chronic hepatic disease and sleep disorder increased the risk of sudden cardiac death in patients aged <35 years. Dementia and organic mental disorder elevated the risk in patients aged between 35-54 years. Ischemic heart disease raised the risk in patients aged ≥55 years. CONCLUSIONS: The risk is increased across the lifespan in schizophrenia, particularly for younger male patients. Furthermore, physical and psychiatric comorbidities have age-dependent risks. The findings suggest that prevention strategies targeted toward sudden cardiac death in patients with schizophrenia must consider the age effect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".