Molecular determinants of neurocognitive deficits in glioma: based on 2021 WHO classification
Bibliographic record
Abstract
Abstract Purpose Cognitive impairment is a common feature among patients with diffuse glioma. This study aimed to investigate the relationship between cognitive function and clinical and molecular factors under the new 2021 WHO classification of tumors of the central nervous system (CNS 5). Methods A total of 110 patients with diffuse glioma were enrolled and underwent preoperative cognitive assessments using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Clinical information was collected from medical records, and gene sequencing was performed to analyze 18 most influenced gene alterations. The differences in cognitive function between patients with and without glioblastoma were compared under both CNS 4 and CNS 5 to assess the effect of differentiation on cognition. Results The study found that age, tumor location, and glioblastoma had significant differences on cognitive function. Several genetic alterations were significantly correlated with cognition. For most focused genes, patients with a low number of genetic alterations tended to have better cognitive function. Conclusion Our study suggested that, in addition to general clinical characteristics such as age, histological type and tumor location, the molecular characteristics of glioma play a crucial role in cognitive function. Further research into the mechanisms by which tumors affect brain function is expected to enhance the quality of life for glioma patients. The findings of this study highlight the importance of considering both clinical and molecular factors in the management of glioma patients to improve cognitive outcomes.
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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 | 0.000 |
| 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".