[Role of magnetic resonance imaging in the early diagnosis of cognitive impairments in patients with type 1 diabetes mellitus].
Bibliographic record
Abstract
OBJECTIVE: To assess the role of brain magnetic resonance imaging (MRI) in patients with type 1 diabetes mellitus (DMI) in relation to clinical, metabolic, and psychoneurological disorders. MATERIAL AND METHODS: Fifty-eight patients aged 16 to 30 years with DM1 were examined; a control group consisted of 29 healthy young people matched by gender and age. Their examination involved clinical, metabolic, and psychological testing. The quality of life was assessed using the general Medical Outcomes Study Short Form (MOS SF-36) and the specific Audit-Dependent Quality of Life (AdDQoL). The Montreal Cognitive Assessment (MoCa test) was employed to screen for cognitive impairments. All the patients were advised by a neurologist. Brain MRI using a 1.0 T Siemens Magnetom scanner was carried out to evaluate structural changes in the central nervous system. RESULTS: The examination of the patients with DM1 revealed the signs of grey matter atrophy, enlarged Virchow-Robin spaces, white matter injury, which correlated with the presence of chronic hyperglycemia, cognitive impairments, and microvascular complications. CONCLUSIONS: Routine brain MRI is best carried out in patients with DMI and poor disease control to timely implement therapeutic-and-prophylactic measures for preventing cognitive impairments and improving the quality of life.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".