Evaluation of Glymphatic System Activity by Diffusion Tensor Image in Mild Traumatic Brain Injury
Bibliographic record
Abstract
To investigate the value of diffusion tensor image analysis along the perivascular space(DTI-ALPS) index on glymphatic system activity in mild traumatic brain injury(mTBI) patients. The study included 28 healthy control participants (HC) (mean age, 34 years) and 28 mTBI patients (mean age, 36 years). The clinical information(including the results of Glasgow Coma Scale(GCS), Montreal Cognitive Assessment(MOCA) and Rivermead behavioral memory test) and MRI data of each subjects were complete. The diffusivity along the perivascular spaces, as well as association and projection fibers, were analyzed to create a diffusivity along the perivascular space index (DTI-ALPS index). Then, we evaluated the variations in the index of ALPS between the HC and mTBI groups, and further correlation analysis between the DTI-ALPS index and clinical features in mTBI patients was carried out. Compared to the HC group, the mTBI group’s ALPS-index values were noticeably lower. [median, whole brain: 1.452 vs. 1.593 (P=0.005); left brain: 1.457 vs. 1.616 (P=0.006); right brain: 1.451 vs. 1.605 (P=0.027)]. Left brain Dyproj values were considerably higher in mTBI patients than in healthy controls (P=0.005). The correlation analysis of the DTI-ALPS index with the GCS, MoCA and Rivermead score in the mTBI group was not statistically significant. DTI-ALPS index can be used to evaluate impairment of the lymphatic system in mTBI patients, which has a good prospect for exploring the pathogenesis of mTBI.
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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".