Correlation of serum ferritin levels with neurological function-related indices and cognitive dysfunction in patients with cerebral hemorrhage
Bibliographic record
Abstract
AIMS: The purpose of this study was to explore the correlation of serum ferritin (FS) levels with neurological function-related indices, such as neuron-specific enolase (NSE) and S100β protein levels, and cognitive dysfunction in patients with cerebral hemorrhage. MATERIALS AND METHODS: Patients with acute non-traumatic cerebral hemorrhage (cerebrovascular disease (VD), n = 128) and healthy controls (CON, n = 128) were included. FS, NSE, and S100β levels were measured using ELISA. Cognitive functions were evaluated using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The receiver operating characteristic (ROC) curve was used to assess the ability of SE, NSE, and serum S100β to predict the diagnosis of cognitive dysfunction in patients with cerebral hemorrhage. Multivariate logistic regression analysis was used to assess the risk factors of cognitive impairment in patients with cerebral hemorrhage. RESULTS: Cognitive impairment in patients with VD was closely related to the increased levels of SE, NSE, and S100β. There was a strong correlation between MoCA and MMSE scores and the levels of FS, NSE, and S100β. The independent risk factors leading to cognitive impairment in cerebral hemorrhage mainly include family history of cerebrovascular disease, body mass index, hypertension, smoking frequency, and elevated levels of low-density lipoproteins, NSE, FS, and S100β. CONCLUSION: NSE, FS, and S100β can be used as important markers for the diagnosis of cognitive impairment in patients with cerebral hemorrhage.
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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".