Abstract WMP31: Lifetime Trauma Exposure Types Are Linked to Stroke Recovery: Findings From the STRONG Study
Bibliographic record
Abstract
Introduction: The STRONG (Stroke, sTress, RehabilitatiON, and Genetics) study has documented the role of cumulative lifetime stress/trauma exposure (LSE) in stroke recovery. Understanding the types of trauma most closely linked with functional outcomes may identify specific risk factors for stroke recovery. We examined specific types of life stress linked with functional and cognitive disability at 1-year post-stroke. Methods: Adults with a new stroke enrolled at 28 US sites were assessed 4 times over 1 year. Assessments included LSE 90 days post-stroke, and Stroke Impact Scale (SIS), modified Rankin Scale (mRS), & Telephone Montreal Cognitive Assessment (tMOCA) at 1-year post-stroke. Bivariate and multivariate analyses examined relationships among LSE and outcomes. Results: The 763 enrollees had age 63.1±14.9 yrs; initial NIHSS score 4 [2-9]; 41.2% Female; 69.4% White. Controlling for age, gender, race, and 3-mo NIHSS score, lifetime exposure to being raped was associated with worse SIS-ADL, mRS, and tMOCA scores 12-mo post-stroke. Several other LSE were also independently associated with worse 12-mo SIS-ADL scores: witnessing a family member be injured or killed, divorce, and emotional abuse. Having a loved one be seriously ill was associated with better 12-mo t-MOCA scores. These findings were robust when early post-stroke acute stress levels were also included in the model. Women were significantly more likely to report being raped, experiencing emotional abuse, and having a seriously ill loved one. Demographics were not significantly associated with divorce or witnessing a family member injured or killed. Conclusions: Assessing specific types of exposure to LSE early in the post-stroke period may help identify at-risk patients who might benefit from additional post-stroke support to enhance their long-term functional and cognitive abilities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".