Brain-derived neurotrophic factor as predictor of early-onset poststroke depression
Bibliographic record
Abstract
Background Poststroke depression (PSD), with an approximately one third prevalence in stroke patients, is associated with increased morbidity and mortality. This study investigated the relationship between serum brain-derived neurotrophic factor (BDNF) levels and early-onset PSD, along with other clinical variables. Methods Clinical data and radiological images of 88 patients diagnosed with acute ischemic stroke were examined. Serum BDNF levels were measured within the first 72 hours following stroke diagnosis. On the 14th day following stroke diagnosis, Montreal Cognitive Assessment (MoCA), Hamilton Depression Rating Scale (HAMD17), and National Institutes of Health Stroke Scale (NIHSS) were administered to the patients. Results Serum BDNF levels ( P = 0.022) and MoCA values ( P = 0.004) of patients with early-onset PSD were significantly lower, and NIHSS values ( P = 0.027) were significantly higher compared to patients without early-onset PSD. There was a significantly negative correlation between BDNF value and HAMD-17 score. Receiver operating characteristic (ROC) analysis was used to investigate the extent that BDNF level could predict early-onset PSD, and cut-off values were determined. For a BDNF cut-off value of 361.5, sensitivity and specificity values were 75% and 56%, respectively, indicating that serum BDNF levels could serve as a useful predictor of early-onset PSD. Conclusion Lower serum BDNF levels are associated with early-onset PSD and may serve as a potential biomarker, although causal conclusions are limited due to the study's cross-sectional design.
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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 | 0.000 |
| 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) | 0.001 | 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".