Proactive Approach in Detecting Elderly Subjects with Cognitive Decline in General Practitioners’ Practices
Bibliographic record
Abstract
BACKGROUND: Although cognitive decline is a common finding among the elderly and is considered a risk factor for developing dementia, it is rarely diagnosed by general practitioners (GPs). AIM: To evaluate cognitive function with the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in asymptomatic subjects in daily GP practice and compare subjects who confirmed having cognitive problems with subjects who did not. METHODS: 388 consecutive subjects >65 years of age who consulted their GP were interviewed and tested with MMSE and MoCA. RESULTS: None of the study subjects spontaneously complained of cognitive or memory problems. 155 subjects (39.94%) confirmed having cognitive problems and 233 (60.05%) did not even when asked. The prevalence of mild cognitive impairment (MCI) was 18.30% (95% CI 14.36-22.04) and the prevalence of cognitive impairment/no dementia (CIND) was 17.27% (95% CI 13.50-21.04). Delayed memory recall as a separate cognitive domain in MoCA was significantly worse in subjects with MCI (p = 0.00958) and in those with CIND (p = 0.0208). CONCLUSION: There is a significant number of patients in daily GP practices with unrecognized, but objectively verifiable, cognitive deficits who do not report having cognitive problems. They can be identified by assessment with MMSE and MoCA already in the GP practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".