Elderly patients with cognitive impairments on an ambulance care
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is one of the most common geriatric syndromes that occur in the elderly. Dementia is a severe cognitive disorder that results in the professional, social, and functional impairment and gradual loss of independence. However, in most cases, the stage of dementia is preceded by a long period of non-dementia cognitive impairment. In this regard, one of the priorities of public health is to identify potentially reversible forms of dementia and cognitive impairment in the early stages. AIM: To assess demographic characteristics, co-morbidities and factors that are associated with cognitive impairment in adults aged 65 years and over and to determine the prevalence of cognitive disorders in aging population. MATERIALS AND METHODS: cross-sectional study included all patients aged 65 years and older who attended the ambulance care from 24.10.2019 to 15.12.2019 in Saint Petersburg. Measurements: the Montreal cognitive assessment test, the 15-item Geriatric Depression Scale. Data collection included a full medical history, blood pressure measurement, a medication review and blood tests (complete blood count, lipids, hormones, glucose, ALT, AST and creatinine). RESULTS: The prevalence of mild cognitive impairment was 62.9 % (95 % CI 56-70), severe cognitive impairment 8.2 %. We detected that hypertension, stroke, sleep disorders, subjective memory complaints and symptoms of depression were identified as factors associated with CI after adjustment for covariates. Hypertension and depression were related with cognitive impairment (p 0.05). Also patients with depression scored worse in global cognition and attention function (p 0.05). Patients with diabetes had association with a decrease in abstraction function (p = 0.02). Low hemoglobin levels were related with poor global cognition and memory impairment (p 0.01). Beta-blocker use was significantly associated with poor global cognition and memory impairment (p 0.01). CONCLUSIONS: We found that elders have a high prevalence of cognitive disorders. We also demonstrated association between co-morbidities and factors as hypertension, anemia, diabetes, depression and administration of beta-blockers with poor cognitive performance in the elderly.
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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) | 0.003 | 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".