Association between microstructural white matter abnormalities and cognitive functioning in patients with type 2 diabetes mellitus: a diffusion tensor imaging study
Bibliographic record
Abstract
Abstract Background Diffusion tensor imaging (DTI) technique is important for exploring more sensitive imaging-based biomarkers in prevention and early treatment of cognitive dysfunction induced by type 2 diabetes mellitus (DM). Objectives To predict early cognitive dysfunction and detection of microstructural white matter changes in patients with type 2 DM by diffusion tensor imaging. Patients and methods A case-control study included thirty patients aged ≥ 18 years old of both sexes with type 2 DM and 30 controls. All subjects underwent to Montreal Cognitive Assessment (MoCA) “Arabic version”: to detect mild cognitive impairment (MCI) and diffusion tensor imaging study (DTI). Results Mild cognitive impairment is related to type 2 DM (56.7% of diabetic group), reduced fractional anisotropy (FA) values, and elevated mean diffusivity (MD) values were related to cognitive impairment evaluated through Montreal Cognitive Assessment (MoCA) in patients with type 2 DM. Conclusion The integrity of the white matter measured using DTI vary in MCI diabetics compared with non-MCI diabetics. Such changes have major implications on the cognitive function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".