Figures of pre and post mean scores of MoCA in Group A and B in type 2 diabetes mellitus
Bibliographic record
Abstract
Background: Diabetes Mellitus (DM) is a chronic metabolic disorder caused byhyperglycemia, impaired insulin secretion, and insulin resistance. Type 2 diabetes mellitus(T2DM) is associated with an increased risk for cognitive dysfunction. Cognitive motor dual-task training forces the brain to process motor tasks in one of the four procedural memorycenters: the basal ganglia, cerebellum, supplementary motor area, and premotor cortex. Hence,it helps improve cognition and motor function in patients with T2DM.Methods: A randomized control study was conducted on 62 subjects with type 2 diabetes mellitus.Pre- interventional measures were measured using the MoCA scale to assess cognition. Theexperimental group [n=31] underwent cognitive motor dual tasks, along with aerobic training.The control group [n=31] received conventional aerobic and resistance exercises. Postinterventional outcomes were measured using the MoCA scale in subjects with T2DM.Results: Statistical analysis of the data revealed that there was a significant improvement incognitive function in experimental group A subjects, with a significant difference observed ingroup A compared to group B. The P value of MoCA was 0.0001 in experimental group Asubjects.Conclusion: Cognitive motor dual-task training (CMDTT) is more effective in increasingcognition in subjects with T2DM. Statistical analysis showed that group A (CMDTT) showedgreater improvement in cognitive function than the control group.Key words: Type 2 diabetes mellitus, cognitive function, cognitive motor dual-task training,Montreal cognitive assessment (MoCA).
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".