Mild Cognitive Impairment among Elderly Persons Residing in an Urban Resettlement Colony in Delhi
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) is a transitional state between normal cognition and clinical dementia. MCI is associated with an increased risk of dementia and mortality. Progression of MCI to dementia can be prevented by cognitive and lifestyle interventions. There is limited evidence on the burden and risk factors associated with MCI in India. To estimate the prevalence of MCI among elderly persons, and to study the factors associated with MCI. Materials and Methods: This community-based cross-sectional study was carried out among 365 persons aged 60 years or older, residing in an urban resettlement colony of Delhi. Participants with dementia (score <23 on the Hindi version of the Mini-Mental State Examination) were excluded. Objective cognitive impairment and functional disability were assessed by the Montreal Cognitive Impairment-Basic (MoCA-B) tool and Barthel's Activities of Daily Living, respectively. The prevalence of MCI was estimated by Petersen's criteria, i.e., subjective memory impairment, objective cognitive impairment (MoCA score 19-25), functional independence, and absence of dementia. Univariate analysis was performed, followed by stepwise multivariate logistic regression. The association of socio-demographic and other health conditions with MCI was assessed. Results: The prevalence of MCI was 9.3% [95% confidence interval (CI) 6.7-12.7], 13.3% (95% CI 8.8-19.7) among men, and 6.5% (95% CI 3.9-10.6) among women. The risk of MCI was higher among current smokers. Conclusions: MCI was common among the elderly. Early detection of MCI may be included in health programs for elderly persons.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".