Understanding risk in the oldest old: Frailty and the metabolic syndrome in a chinese community sample aged 90+ years
Bibliographic record
Abstract
Objectives To investigate the relationship between frailty and the metabolic syndrome and to evaluate how these contribute to mortality in very old people. Design Secondary analysis of data from the Project of Longevity and Aging in Dujiangyan. Setting Community sample from Sichuan Province, China. Participants People aged 90+ years (n=767; baseline age=93.7±3.4 years; 68.0% women. Measurements After a baseline health assessment , participants were followed for four years (54.0% died). A frailty index (FI) was calculated as the sum of deficits present, divided by the 35 health-related deficits considered. Relationships between the FI and the metabolic syndrome were tested; their effect on death was examined. Results The mean FI was 0.26 ±0.11. Higher FI scores were associated with a greater risk of death, adjusted for age, sex, education, and metabolic syndrome items. The hazard ratio was 1.03 (95% confidence interval 1.02, 1.04) for each 1% percent increase of the FI. The mortality risk did not change with the metabolic syndrome (odds ratio=0.99; 0.71-1.36). Conclusions In the oldest old, frailty was a significant risk for near-term death, regardless of the metabolic syndrome. Even using age-adjusted models, the epidemiology of late life illness may need to account for frailty routinely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".