Mild Cognitive Impairment and Hand Dexterity in Individuals with Type 2 Diabetes Mellitus: A Cross-sectional Study
Bibliographic record
Abstract
Introduction: Type 2 Diabetes Mellitus (T2DM) presents various complications, but Mild Cognitive Impairment (MCI) has recently gained considerable attention due to its potential progression to dementia. Hand dexterity is highlighted as crucial for cognitive tasks, suggesting that even minor cognitive impairments can impact daily activities. Despite this, there has been no exploration of the relationship between MCI and hand dexterity in individuals with T2DM. Aim: To determine the relationship between MCI and hand dexterity in people with T2DM. Materials and Methods: This cross-sectional study included 45 diabetic patients aged 40-60 years from Justice KS Hegde Charitable Hospital in Mangaluru, Karnataka, India. The study was conducted from May 2023 to March 2024. Participants were recruited based on specific inclusion criteria and screened using tools such as the Semmes Weinstein Monofilament and the Montreal Cognitive Assessment (MoCA). Subsequently, selected participants underwent the hand dexterity test using the Box and Block Test (BBT) and their performance values were recorded. The categorical variables were presented as frequency and percentage. The continuous variables were presented as mean±SD. Correlation was performed using Pearson’s correlation coefficient. Linear regression was conducted for significant correlations. A p-value <0.05 was considered statistically significant. Results: Individuals in the study scored lower than expected values on both right and left hand tasks compared to normative scores. Moderate positive correlations were found between BBT scores and MoCA scores for both right (r-value=0.469, p-value=0.001) and left (r-value=0.516, p-value <0.001) hand tasks with p-value <0.05. Conclusion: The study reveals a significant positive correlation between BBT and MoCA scores. This suggests that hand dexterity is closely linked to cognitive function, emphasising the importance of integrating manual dexterity assessments into cognitive evaluations, particularly in populations where both motor and cognitive abilities are clinically relevant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".