Delirium and Previous Psychiatric History Independently Predict Poststroke Posttraumatic Stress Disorder
Bibliographic record
Abstract
OBJECTIVES: Delirium is an acute brain dysfunction that has been correlated with adverse mental health outcomes, such as depression and posttraumatic stress disorder (PTSD). However, delirium has not been studied in relation to mental health outcomes after cerebrovascular events. This study aimed to examine the incidence of PTSD after nontraumatic intracerebral hemorrhage (ICH) and identify new predictors of poststroke PTSD symptoms. METHODS: Clinical data were collected from 205 patients diagnosed with nontraumatic ICH. Demographics and hospital course data were examined. Univariate and multivariable correlational analyses were performed to determine predictors of PTSD symptoms. PTSD symptoms were assessed using PTSD checklist-civilian version (PCL-C) scores. RESULTS: Diagnostic criteria for a positive PTSD screen (PCL-C score ≥44) were met by 13.7%, 20.2%, and 11.6% of nontraumatic patients with ICH at 3, 6, and 12 months, respectively. On univariate analysis, younger age, female sex, unemployed, and in-hospital delirium were correlated with higher PCL-C scores. In multivariable models, younger age, female sex, unemployed, in-hospital delirium, and a previous anxiety or depression diagnosis were associated with higher PCL-C scores at different follow-up times. Modified Rankin Scale scores were also positively correlated with PCL-C scores at each time point. CONCLUSIONS: Delirium, previous psychiatric history, younger age, female sex, and unemployment status were found to be associated with a greater degree of posthemorrhagic stroke PTSD symptoms. More significant PTSD symptoms were also correlated with greater functional impairment. A better understanding of patient susceptibility to PTSD symptoms may help providers coordinate earlier interventions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".