Relationship Between Depression After Hemorrhagic Stroke and Auditory Event-Related Potentials in a Chinese Patient Group
Bibliographic record
Abstract
Purpose: Post-stroke depression (PSD) is the most common psychiatric sequelae of stroke. Numerous studies revealed that event-related potentials (ERP) can reflect depression severity to a certain extent, while there is almost no research on depression after hemorrhagic stroke. Therefore, we employed a prospective cross-sectional study to explore the relationship between ERP and depression after hemorrhagic stroke. Methods: A total of 74 patients with intracranial hemorrhage were included in this study. Neurological deficits were evaluated using the National Institutes of Health Stroke Scale (NIHSS) on admission. Depression severity and cognitive impairment were measured using the 17-item Hamilton Depression Scale (HAMD-17) and the Chinese version of the Montreal Cognitive Assessment (MoCA) after two weeks of treatment. All patients were conducted auditory Oddball paradigm for event-related potential mismatch negativity (MMN) and P300. Results: In total, 36 patients were diagnosed with PSD at the two weeks of treatment, for a percentage of 48.6%. Depression severity of ICH patients correlated positively with both the latency of MMN (r = 0.376, P = 0.001) and P300 (r = 0.325, P = 0.005), and correlated negatively with both the amplitude of MMN (r=-0.385, P = 0.001) and P300 (r=-0.311, P = 0.007). Depression severity was negatively correlated with cognitive function after hemorrhagic stroke (r=-0.347, P = 0.002). Conclusion: The latency and amplitude of MMN and P300 can well reflect the degree of depression after hemorrhagic stroke, which may help in the early diagnosis and effective treatment of PSD.
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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 | 0.001 | 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) | 0.001 | 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".