Abstract TP414: Early and Delayed Onset Poststroke Depression
Bibliographic record
Abstract
Background and aim: We aimed to investigate the predictors for early (3-month) and delayed (3-year) depression after stroke. Methods: Stroke or transient ischemic attack (TIA) subjects (n=1023) were recruited from the C hinese U niversity of Hong Kong - ST roke R egistry I nvestigating cognitive DE cline (CU-STRIDE). Clinical factors, drug use and neuroimaging markers of small vessel diseases (SVD) were collected at baseline. Geriatric depressive score (GDS) 15-items (≥ 6) was used to ascertain the presence of depression. Logistic regression was used to explore risk factors for presence of depression at 3-month and delayed-onset depression at 3-year, respectively. Results: A total of 394 (38.5%) patients had depression at 3-month after stroke/TIA, leaving 629 depression-free subjects. Among these 629 subjects, 350 received re-assessment of GDS at 3-year, and 49 (14%) developed depression. At 3-month, National Institute of Health Stroke Scale (NIHSS) score (OR=1.09, 95% CI: 1.04-1.14, p<0.001), Montreal Cognitive Assessment performance (MoCA) (OR=0.92, 95% CI: 0.89-0.96, p<0.001), and younger age (OR=0.979, 95% CI: 0.961-0.997, p=0.024) were associated with depression, while statins use (OR=0.69, 95% CI: 0.49-0.96, p=0.028) was associated with a decreased risk. At 3-year, presence of lacunes at baseline (OR=2.2, 95% CI: 1.1-4.3, p=0.022) and MoCA performance (OR=0.88, 95% CI: 0.81-0.96, p=0.003) were significant risk factors for development of delayed-onset depression, while long-term use of statins (OR=0.4, 95% CI: 0.2-0.9, P=0.021) was associated with a reduced risk of delayed-onset depression. Conclusion: Clinical severity and age influence early-onset depression, while SVD and cognitive impairment increase risk of delayed-onset depression. Statins use protects from development of depression.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".