Abstract TP86: Depression And Anxiety After Stroke In A Young Adult Population
Bibliographic record
Abstract
Background and Objectives: We aimed to determine the prevalence of poststroke depression (PSD) and anxiety as well as to identify their predictors in a population of young Filipino adults. Methods: We performed a cross-sectional epidemiologic study in the largest tertiary hospital in the Philippines. The study involved the administration of a structured survey tool and review of medical records. The Hospital Anxiety and Depression Score was used to screen for anxiety and depression. Chi-square tests and Fisher’s exact tests were used to compare between groups with anxiety or depression and those without. Multivariable logistic regression analysis was performed to determine significant socio-economic and clinical risk factors of PSD and anxiety. Results: In our study population of 114 young adult stroke patients, the prevalence of depression was 20.2% while the prevalence of anxiety was 34.2%. Significant predictors of PSD were the presence of anxiety (OR 1.84; CI 1.05-3.22), lower mRS scores (mRS 3-5 OR 5.52; 95% CI 1.09-8.03) and diabetes (OR 2.09; 95% CI 1.67-6.26). Meanwhile, significant predictors of poststroke anxiety included depression (OR 7.5; 95% CI 5.02-21.94) and dependence (Barthel Index scores 95-100; OR 0.32; 95% CI 0.14-0.71). Relationship status, educational attainment, stroke subtype and stroke location were not found to be significant predictors of PSD and anxiety. Conclusion: Our study showed that young adult patients who suffer from stroke are at risk for adverse socio-economic and psychiatric consequences. A significant proportion of the population had depression and anxiety after stroke. Clinicians should be aware of these conditions that influence the outcomes of young adult stroke patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".