Diffusion tensor imaging of white matter in patients with prediabetes by trace‐based spatial statistics
Bibliographic record
Abstract
BACKGROUND: Prediabetes is an intermediate state in which blood glucose is higher than normal but does not meet the diagnostic criteria for diabetes. Diffusion tensor imaging (DTI) is an MRI method that can sensitively detect microscopic lesions in the white matter of the brain, but most previous DTI studies only focused on patients with diabetes, and little is known about prediabetes. PURPOSE: To investigate the changes in the microstructure of brain white matter in prediabetes patients using DTI and trace-based spatial statistics (TBSS). STUDY TYPE: Prospective. POPULATION: Sixty subjects (30 patients with prediabetes and 30 healthy volunteers) were enrolled. FIELD STRENGTH/SEQUENCE: 3.0T/DTI-MRI sequence with single-shot echo-planar imaging sequence (SE-EPI). ASSESSMENT: DTI data were collected and analyzed using the TBSS method in the FMRIB software library. STATISTICAL TESTS: DTI using a two-sample t-test. Pearson correlation analysis was performed on DTI values and neuropsychology scale results (mini-mental state examination [MMSE], Montreal cognitive assessment [MoCA], self-rating anxiety scale [SAS], and self-rating depression scale [SDS]) RESULTS: Compared with the control group, the fractional anisotropy (FA) values in the right part of the corpus callosum body (bCC) (P = 0.035), the right superior longitudinal fasciculus (SLF.R) (P = 0.047), and the left superior longitudinal fasciculus (SLF.L) in the prediabetic group were reduced (P = 0.040). DATA CONCLUSION: DTI as a noninvasive technique can assess early changes in the white matter microarchitecture of patients with prediabetes. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1105-1112.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".