Lower serum BDNF as a predictor of post-stroke cognitive impairment in acute ischemic stroke patients
Bibliographic record
Abstract
Background: Reduced level of serum BDNF in acute stroke patients is associated with poor outcomes. We aimed to identify the role of serum BDNF level as a predictor for post-stroke cognitive impairment (PSCI). Methods : This was a prospective study. We recruited acute ischemic stroke patients in Dr. Sardjito General Hospital Yogyakarta, Indonesia followed them up for 90 days (3 months). Serum BDNF was collected at day 5 and day 30 of stroke onset and measured by ELISA. Montreal Cognitive Assessment (MoCA) was used to measure the cognitive function at 90 days of follow up. ROC curve was conducted to measure the cut-off point of the BDNF level. Factors independently associated with PSCI were analyzed by using stepwise regression. Results: Among 89 patients recruited, 60 patients (67.41%) developed PSCI. The mean age of PSCI and non-PSCI patients was 62.7 ± 9.5 and 57.5 ± 8.7, respectively (p = 0.01). Patients with dyslipidemia were less likely to develop PSCI (OR 0.19, 95%CI 0.06–0.56, p < 0.05). In addition, patients with day 5-serum BDNF level < 23.29 ng/mL were five times more likely to develop PSCI compared with their counterparts (OR 5.02, 95%CI 1.67–15.04, p < 0.05). Conclusion s: Among acute ischemic stroke patients, those with serum BDNF <23.29 ng/mL had a higher risk of developing PSCI, while those with dyslipidemia had a lower risk of PSCI. This study suggests that BDNF could be a predictor of PSCI, allowing for earlier detection and better preventive strategies.
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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 | 0.001 |
| 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".