Effect of radiochemotherapy on the cognitive function and diffusion tensor and perfusion weighted imaging for high-grade gliomas: A prospective study
Bibliographic record
Abstract
This study aimed to explore the effects of radiochemotherapy on the neurocognitive function of patients with high-grade gliomas (HGG). The mini-mental state examination (MMSE), Montreal Cognitive Assessment (MoCA), event-related potential P300 (ERP-P300), and specific MRI parameters were compared, and the associations between specific MRI parameters and different doses of radiation were determined for before and up to 12 months after radiotherapy. There were no significant differences in MMSE, MoCA, or ERP-P300 before and after radiotherapy. Compared with pre-radiochemotherapy, fractional anisotropy (FA) in the contralateral hippocampus decreased at 6 and 9 months after radiotherapy. FA in the ipsilateral hippocampus before radiochemotherapy decreased compared with 6 months after radiotherapy. Compared to the end of radiotherapy, as well as 3- and 6-months post-radiotherapy, the regional cerebral blood volume (rCBV) in the genu of the corpus was significantly lower at 12 months post-radiotherapy. Some MRI parameters in different regions of the brain were negatively correlated with the mean and maximum dose. There was no significant effect of radiochemotherapy on the neurocognitive functioning of patients with HGGs found before radiochemotherapy until 12 months after radiotherapy. The radiation-induced FA decrease in the bilateral hippocampus preceded cognitive dysfunction, and DTI of the hippocampus may provide a useful biomarker for predicting radiation-induced neurocognitive impairment in patients with HGGs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".