Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0964
version: 2
kind: revised
reason: Direction audit: the problem line stated no AI consequence and was removed. Filed as context; not counted as a gain or a problem.
timestamp: 2026-10-26T06:38:50.656868Z
status: published
lens: general
sector: health
headline: Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations
dek: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post-traumatic stress disorder-despite evidence that a range of psychiatric disorders may follow trauma. We applied machine learning to identify key post-traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population-based cohort. Methods We conducted a case-control study of…
gain_title: (none)
problem_title: Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk.
problem_evidence: (none)
quick_read: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post-traumatic stress disorder-despite evidence that a range of psychiatric disorders may follow trauma.

We applied machine learning to identify key post-traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population-based cohort. Machine learning model predictors included a range of psychiatric disorders diagnosed ≤3 years following trauma.
limitation: 
tag: Context
key_points: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. | However, most research has specifically examined post-traumatic stress disorder-despite evidence that a range of psychiatric disorders may follow trauma. | We applied machine learning to identify key post-traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population-based cohort.
rundown: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post-traumatic stress disorder-despite evidence that a range of psychiatric disorders may follow trauma.

We applied machine learning to identify key post-traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population-based cohort. Methods We conducted a case-control study of adults aged ≥18 years, nested within a trauma-exposed cohort of all Denmark residents from 1995 to 2018.
sources:
- peer_reviewed | Journal of the American Heart Association | https://doi.org/10.1161/jaha.126.049492 | 2026-09-02
prev: a05390dcc8fce9aa580d95787bea9d800853bfc26009166223bbdab2b4f5ead1

sha256

3cd073237e45583499a904e61a05f4cef8cff8a26097d63402623a763d84fac7

previous

a05390dcc8fce9aa580d95787bea9d800853bfc26009166223bbdab2b4f5ead1