Characteristics of an elderly patient with cognitive impairments in outpatient practice
Bibliographic record
Abstract
Aim: to study the prevalence of cognitive impairments (CI) in patients over 65 y.o., determine concomitant diseases and factors affecting cognitive functions (CF). Patients and Methods: a single-stage cross-sectional study of a continuous sample of patients (over 65 y.o. who applied for outpatient care from 24.10.2019 to 15.12.2019) in St. Petersburg was conducted. The study methods included: Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), blood pressure (BP) test, the study of outpatient medical histories, assessment of pharmacological treatment, laboratory studies (clinical blood count, lipid profile, hormonal studies, blood glucose level, transaminases and creatinine level). Results: the prevalence of mild cognitive impairment (MCI) among the study subjects was 62.9% (95% CI 5670), severe cognitive impairment (SCI) — 8.2%. After covariate adjustment, hypertension, acute cerebrovascular accident (ACVA), complaints of poor sleep quality, subjective cognitive complaints, and depression symptoms were associated with a decrease in CF. The male sex was associated with a decrease in memory function (p=0.03), hypertension — with a decrease in total CF, memory function (p=0.03) and constructive praxis (p=0.01), depression — with a decrease in total CF and indicators of attention mechanism (p<0.01), diabetes mellitus (DM) — with a decrease in thinking function (p=0.01), reduced hemoglobin level — with a decrease in total CF and memory function (p<0.01), β-blockers intake — with a decrease in total CF, the function of verbal fluency (p=0.01), thinking (p<0.01) and memory (p<0.01). Conclusion: the high prevalence of CI was revealed among elderly and senile patients who sought outpatient care. The association of hypertension, anemia, DM, depression symptoms and β-blockers intake with CF deterioration in the elderly and senile age was determined. KEYWORDS: elderly and senile age, cognitive disorders, dementia, risk factors, screening for cognitive impairment, MoCA. FOR CITATION: Turusheva A.V., Bogdanova T.A., Frolova E.V., Logunov D.L. Characteristics of an elderly patient with cognitive impairments in outpatient practice. Russian Medical Inquiry. 2021;5(10):618–622 (in Russ.). DOI: 10.32364/2587-6821-2021-5-10-618-622.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".