Bibliographic record
Abstract
Objective To study impact of cognitive impairment on the life expectancy(LE),active life expectancy(ALE) and ALE/LE ratios in ≥ 60-year Beijing urban and rural population.Methods The samples were derived from Beijing Multidimensional Longitudinal Study on Aging.The subjects were the ≥ 60-year population from Xuanwu district(urban areas),Daxing district(plain) and Huairou district(mountain areas) in Beijing.The assessment of cognitive function was conducted in 2111 subjects in 2009 by using the Mini Mental State Scale.The samples were followed up in 2011.Health or not was assessed according to whether the patients independently completed the activities of daily living(ADL).The LE,ALE and ALE/LE in each age stage in a cognitive impairment group and the non-cognitive impairment group were calculated by using the multi-state life table(IMaCH software).Results There were 312 old people with cognitive impairment among the 2111 elderly.①Both LE and ALE of the elderly women in Beijing were higher than men,and urban population had a higher LE and ALE than rural areas.The LE、ALE and ALE/LE of the elderly with cognitive impairment were lower than normal cognitive group.②In the cognitive impairment group,the LE and ALE of the urban younger aged male(70 years) were significantly lower than the same age group of women.Regardless of region and gender,people over 80 years had the lowest LE and ALE.LE and ALE of the female decreased more than that of male.③The ratio of ALE/LE of male was higher than female,as well as higher in urban elderly.The ALE/LE ratio was descending along with ageing in both cognitive impairment group and normal cognitive group,and the speed of reduction was much faster in the cognitive impairment group,especially in suburban female population over 80 years old had the lowest ALE/LE ratio.Conclusion Cognitive impairment remarkably impacted the active life expectancy on senior citizens living in Beijing,especially for 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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".