Clinical, Neuroimaging, and Genetic Markers Associated with Cognitive and Functional Outcomes After Traumatic Brain Injury
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) is a major cause of long-term disability worldwide, often leading to progressive cognitive and functional impairments. This study aimed to investigate the underlying factors contributing to long-term deterioration in TBI patients. Methods: We conducted a comprehensive evaluation of 145 patients aged 18–66 years with a documented history of TBI and ongoing cognitive and behavioral deficits. Assessments included neuroimaging, laboratory tests, genetic analysis, and standardized tools such as the Montreal Cognitive Assessment (MoCA) and the Barthel Index. Results: Structural brain abnormalities, including ventricular enlargement and gliosis, were observed in a substantial portion of the cohort. Persistent neuroinflammatory markers were also identified. Genetic analysis revealed a significant association between cognitive decline and polymorphisms in the ACE and PON1 genes. Patients carrying these variants were more likely to exhibit reduced cognitive performance and greater functional limitations. Conclusion: These findings suggest that genetic predisposition, chronic neuroinflammation, and structural brain damage collectively contribute to long-term outcomes following TBI. This highlights the potential of genetic and imaging biomarkers in identifying high-risk individuals and supports the need for personalized approaches to diagnosis, monitoring, and treatment in chronic TBI management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".