Is Serum VEGF-A Level an Indicator of Early-Onset Poststroke Depression?
Bibliographic record
Abstract
Background and Objectives: Poststroke depression (PSD) is a psychiatric complication occurring after a stroke, and is known to negatively impact quality of life. In the present study, the possible relationship between serum vascular endothelial growth factor (VEGF-A) levels and early-onset PSD, as well as the predictive value of serum VEGF-A levels for early-onset PSD, were investigated. Materials and Methods: The study included 88 individuals diagnosed with acute ischemic stroke (AIS). Demographic data, clinical characteristics, and serum VEGF-A levels were recorded, and radiological images were examined to determine the lesion locations. The National Institutes of Health Stroke Scale (NIHSS), Montreal Cognitive Assessment (MoCA), and Hamilton depression scale (HAMD-17) were administered to the patients. Furthermore, serum VEGF-A levels were measured in all participants. Results: Although the body mass index (BMI) and VEGF-A levels were similar between the groups, MoCA scores were lower [(19.2 ± 4.4) vs. (22.3 ± 3), p = 0.001] and NIHSS scores were higher [18 (8–28) vs. 14 (3–24), p = 0.006] in individuals with PSD than in those without it. When the patients with PSD were categorized into three groups, patients with severe PSD had higher NIHSS scores [26 (23–27) vs. 15 (8–23), p = 0.006] and lower MoCA scores [(14.3 ± 1) vs. (20.9 ± 3.8), p = 0.005] than those with mild PSD. Moreover, VEGF-A levels and lesion localization were similar between mild, moderate, and severe PSD groups (p = 0.130). The MoCA score was negatively (r = −0.498, p < 0.001) correlated and the NIHSS score was positively correlated (r = 0.497, p < 0.001) with the HAMD-17 score. Conclusions: Our findings suggest that longitudinal studies in large cohorts including healthy control groups are needed to examine the possibility of using serum VEGF-A level as a marker for predicting early-onset 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 | 0.002 |
| 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 | 0.001 | 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".