Synthetic quantitative magnetic resonance imaging and quantitative susceptibility mapping to reveal brain function in Type 2 Diabetes
Bibliographic record
Abstract
Motivation: Identification of high-risk dementia in T2DM is very important for early intervention. Goal(s): To explore a feasible imaging approach in early discovering T2DM-driven cognitive impairment. Approach: To compare ROI-based retrieved relaxation time of synthetic magnetic resonance imaging and quantitative susceptibility values between T2DM and healthy cohorts (NCs) and also correlate significant differences of all measurements with Montreal Cognitive Assessment (MoCA) scores. Results: T2DM had higher T1 and T2 relaxation time, PD and QS values in some brain regions than NCs. Moreover, T1 value of left insula was negatively associated with MoCA. Impact: Synthetic MRI and QSM can detect abnormal brain areas associated with in T2DM, and the former had more potential in clinically diagnosing early alteration in T2DM due to more direct visualization and measurements on the scanner console.
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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 | 0.002 |
| 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".