Development of a quantitative statistical analysis system for double inversion recovery (DIR) MRI: A preliminary clinical study
Bibliographic record
Abstract
BACKGROUND: Gray matter (GM) imaging is important in the investigation of many neurological diseases, including schizophrenia, multiple sclerosis, stroke, Alzheimer's disease, tuberous sclerosis, and epilepsy, which are all associated with changes in cortical GM. OBJECTIVE: The aim of this study was to develop a quantitative statistical analysis system for double inversion recovery (DIR) MRI and to evaluate the new system using preliminary clinical data. METHODS: The study population comprised of 10 healthy volunteers and six patients with or without brain degeneration. A quantitative statistical analysis system for DIR images was developed using the following steps: 1) brain spatial normalization, 2) mean and standard deviation (SD) map creation, and 3) Z-score map creation. To evaluate the new voxel-based morphometry system, Z-scores of lesions in patients with brain degeneration were measured and then compared with Z-scores of normal regions. RESULTS: All DIR images were adequately spatially normalized to Montreal Neurological Institute MNI coordinate. Lesions in each patient were indicated by high Z-score values on a Z-score map, which were significantly higher than Z-scores of normal regions (p< 0.05). CONCLUSIONS: In this study, we developed a quantitative statistical analysis system for DIR MRI. Using our system, clinicians might accurately diagnose early brain degeneration.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".