Comparison of early and late diagnosis impact of type 2 diabetes on cognitive function: a pilot study
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (T2DM) is known to be associated with cognitive impairment, but the Impact of the timing of diagnosis on cognitive function remains unclear. This pilot project aims to assess the cognitive function of people diagnosed with T2DM at an early vs. late stage. The study will examine several cognitive domains, such as attention, memory, executive function, visuospatial skills, and sensorimotor abilities. Methods: We recruited 80 adults diagnosed with T2DM, evenly split into 2 groups-one with early diagnosis (≤5 years) (n=40) and other with late diagnosis (≥6 years) (n=40) depending on when their disease was identified. Both groups underwent evaluation for demographic and clinical factors. Cognitive function was assessed using mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), and Addenbrooke's cognitive examination (ACE-III). Specific domain of cognition wasmeasured as span of attention (Tachitoscope), memory (PGI Battery scale), executive function (Stroop test), visuospatial function (Corsi block test), sensorimotor abilities (auditory /visual reaction time), and intelligence (Koh’s Block design test). Results: Preliminary findings suggest that the early diagnosis group showed significantly average cognitive performance compared to the late diagnosis group. They also showed improved metabolic control and increased levels of physical activity. Individuals in the early diagnosis group had higher educational levels and socioeconomic status, potentially leading to improved disease detection and more effective health management. Conclusions: These findings indicate that identifying T2DM at an early stage, help in preserving cognitive function as compared to a diagnosis made at a later stage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".