An analysis of cognitive status for elderly people with subjective memory complaint in seven community of Shapingba district in Chongqing city
Bibliographic record
Abstract
Objective To explore the cognitive status of community-dwelling older adults with subjective complaints (SMC). Methods A total of 262 older adults with SMC were included based on convenience sampling. Face-to-face interview was conducted for demographic information collection and cognitive assessment. Statistical analyses were undertaken using SPSS version 19.0. Results The participants with SMC had a low level of general cognitive function, with a mean score of 22.18±4.21 on the MoCA; the two cognitive domains of abstraction and delayed memory had the lowest scores, with an average score of 0.85±0.82 and 2.91±1.50 respectively, and the average scoring rate lower than 60.00%. About 54.20% of the older adults with SMC presented with cognitive impairment. The scores of abstraction were low in the participants with and without cognitive impairment. In addition, the scores of visuospatial/executive function , language and delayed memory were low in the participants with cognitive impairment. The cognitive impairment of persons with SMC may be associated with age and marital status. The people over the age of 70 years and widowed ones are more likely to suffer from cognitive dysfunction. Conclusion The older adults with SMC have a low level of general cognitive function, and cognitive impairment detection rate is higher. Advanced age and widowhood are important factors affecting their cognitive function. Key words: Subjective complaint; Cognitive function; Community; 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".