Type-2 diabetes mellitus reduces cortical thickness and decreases oxidative metabolism in sensorimotor regions after stroke
Bibliographic record
Abstract
Individuals with type-2 diabetes mellitus experience poor motor outcomes after ischemic stroke. Recent research suggests that type-2 diabetes adversely impacts neuronal integrity and function, yet little work has considered how these neuronal changes affect sensorimotor outcomes after stroke. Here, we considered how type-2 diabetes impacted the structural and metabolic function of the sensorimotor cortex after stroke using volumetric magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). We hypothesized that the combination of chronic stroke and type-2 diabetes would negatively impact the integrity of sensorimotor cortex as compared to individuals with chronic stroke alone. Compared to stroke alone, individuals with stroke and diabetes had lower cortical thickness bilaterally in the primary somatosensory cortex, and primary and secondary motor cortices. Individuals with stroke and diabetes also showed reduced creatine levels bilaterally in the sensorimotor cortex. Contralesional primary and secondary motor cortex thicknesses were negatively related to sensorimotor outcomes in the paretic upper-limb in the stroke and diabetes group such that those with thinner primary and secondary motor cortices had better motor function. These data suggest that type-2 diabetes alters cerebral energy metabolism, and is associated with thinning of sensorimotor cortex after stroke. These factors may influence motor outcomes after stroke.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".