Investigating the Prevalence and Determinants of Mild Cognitive Impairment in the Elderly Population at Primary Care Facilities
Bibliographic record
Abstract
Aim: To investigate the prevalence and determine profile of patients with mild cognitive impairment (MCI) among older adults attended at the first level of care and the possible factors associated with MCI. Study Design: Observational, cross-sectional and analytical study. Methodology: The study was conducted with Mexican patients attending the outpatient consultation of the Gerontology Speciality at the Family Medicine Clinic “División del Norte” (an Ambulatory Care Medical Unit), in Mexico City. Data was collected through a protective design using the Montreal Cognitive Assessment test and a structured survey on sociodemographic factors. A descriptive statistical analysis and univariate and multivariate logistic regression models were performed. Results: The median age was 72 years old (IQR=66-78 years). The youngest participant was 60 years old and the oldest was 93 years old (range=33 years). The elderly population with MCI are female, septuagenarian, with a basic level of education. The prevalence of MCI was 28%, and 18% for dementia. The factors that increase the risk of MCI are: age (OR=1.072, 95% CI 1.034-1.111), hypertriglyceridemia (OR=13.709, 95%CI 1.267-148.294), peptic ulcer disease (OR=5.92, 95%CI 1.009-34.719), glaucoma (OR=4.048, 95%CI 1.051-15.596), chronic obstructive pulmonary disease (OR=5.616, 95%CI 1.024-30.802), and asthma (OR=12.323, 95%CI 1.128-134.578). The high educational level was associated as a protective factor (OR=0.336, 95%CI 0.189-0.596). Conclusion: Prevention programmes are necessary to avoid MCI, along with interventions to improve patients' quality of life, and the promotion of educational and engaging activities to support cognitive health in elderly people.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".