Abstract WP305: Childhood Trauma Exposure, Loneliness, Mental Health, and Stroke Recovery: Findings From The STRONG Study
Bibliographic record
Abstract
Introduction: Childhood trauma exposure (CTE) is a known risk factor for poor adult mental&physical health, higher mortality, and disability. Understanding the psychosocial mechanisms by which CTE affects functional outcomes could help identify intervention targets to improve outcomes after stroke. We examine post-stroke mental health and loneliness as potential mediators of the association between CTE and functional/cognitive disability at 1-year post-stroke. Methods: Adults with a new stroke enrolled in the STRONG ( S troke, s T ress, R ehabilitati ON , and G enetics) study at 28 US sites and were assessed 4 times over 1 year. Assessments included CTE, mental health (depression, anxiety, PTSD), and loneliness 90 days post-stroke, and Stroke Impact Scale (SIS), modified Rankin Scale (mRS),&Telephone Montreal Cognitive Assessment (tMoCA) 1-year post-stroke. Analyses examined 90-d mental health and loneliness as mediators of the link between CTE and 1-year outcomes. Results: The 763 enrollees had age 63.1±14.9 years; initial NIHSS score 4 [2-9]; 41.2% Female; 69.4% White. Complete case (N=332) analysis revealed that controlling for age, gender, race, and acute NIHSS score, CTE was not directly associated with any 1-year functional outcome but was significantly associated with loneliness and mental health symptoms 3-months post-stroke. However, greater CTE was indirectly and significantly linked to worse 1-year mRS and SIS scores through 3-mo loneliness, and to worse 1-year mRS, SIS, and tMOCA scores through 3-mo mental health symptoms. Conclusions: Mental health and loneliness are pathways through which CTE is linked to post-stroke functional outcomes. Addressing mental health symptoms and loneliness early post-stroke may support improved functional outcome 1-year post-stroke in patients who have experienced CTE.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".