Proactive Approach in Detecting Elderly Subjects with Cognitive Decline in General Practitioners’ Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it