3T MR with diffusion tensor imaging and single‐voxel spectroscopy in giant axonal neuropathy
Bibliographic record
Abstract
Magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and MR spectroscopy (MRS) data were obtained in a patient with giant axonal neuropathy (GAN) and compared to a control group. Fractional anisotropy (FA) and apparent coefficient diffusion (ADC) data were obtained from specific white matter tracts including the corticospinal tracts (CST), corpus callosum (CC), optic radiations (OR), and middle cerebellar peduncle (MCP). Analysis of the MRS was performed. DTI parameters and MRS results were correlated with the neuropathological findings described for GAN. No significant difference between the FA of the CC of the patient and the control group was found. However, there was a significant difference between the FA of the CST, OR, and MCP of the patient and the control group. The ADC values for all tracts of the patient were significantly increased. N-acetylaspartate to creatine (NAA/Cr) and N-acetylaspartate to choline (NAA-Cho) (choline) metabolite ratios were slightly decreased and choline to creatine (Cho/Cr) and myo-inositol to creatine (Ins/Cr) metabolite ratios were increased in the parietal gray and white matter of the patient as compared to the control group. Cerebellar involvement was less evident. The DTI and MRS findings suggest myelin and axonal damage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".