Cognitive impairment in type 2 diabetes : Opportunities for diagnosis, prevention and management
Bibliographic record
Abstract
In the Netherlands, hundreds of thousands of people aged 70 years or older are currently living with type 2 diabetes, and these numbers are expected to increase further. Older people with type 2 diabetes have an increased risk of cognitive impairment, including both mild cognitive impairment and dementia. We studied the impact of cognitive impairment on people with type 2 diabetes. We found that, besides a higher risk of cardiovascular events and death, people with diabetes have more depressive symptoms and visit the emergency room and the general practice out-of-hours services more often when they have cognitive impairment. We conclude that this is a vulnerable group that may benefit from tailored diabetes care. We also investigated possible starting points for the prevention of cognitive impairment in type 2 diabetes. We found that both low and high mean blood glucose levels are related to poor cognitive functioning, particularly in older women. Furthermore, we focused on the question “how should general practitioners identify cognitive impairment?”. Until now, all people who visit their general practitioner with cognitive complaints are offered the same cognitive test, the MMSE. Using three different cognitive tests (the clock-drawing-test, the MoCA and the MMSE), with the choice of the test depending on the chance that the patient has cognitive impairment, offers the opportunity to provide people more accurate answers to questions about their cognitive functioning. Finally, we demonstrated that the ‘Test-Your-Memory’ and ‘Self-Administered-Gerocognitive-Examination’ are suitable tests to screen for cognitive impairment in people with type 2 diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".