Sex Differences in the Limit to Deficit Accumulation in Late Middle-Aged and Older Chinese People: Results From the Beijing Longitudinal Study of Aging
Bibliographic record
Abstract
BACKGROUND: On average, as people age, they accumulate more health deficits and have an increased risk of death. The deficit accumulation-based frailty index (FI) can quantify health and its outcomes in aging. Previous studies have suggested that women show higher FI values than men and that the highest FI score (the "limit to frailty") occurs at a value of FI ~ 0.7. Even so, gender differences in the limit to frailty have not been reported. METHODS: Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 3,257 community-dwelling Chinese people, aged 55+ years at baseline. The main outcome measure was 5-year mortality. An FI consisting of 35 health-related variables was constructed. The absolute and 99% FI limits were calculated for different age groups and analyzed by sex. RESULTS: The mean level of the FI increased with age and was lower in men than in women (F = 67.87, p < .001). The 99% FI limit leveled off slightly earlier with a relatively lower value in men (60 years; 0.44 ± 0.02) compared with that in women (65 years; 0.52 ± 0.04). The highest absolute FI value was 0.61 in men and 0.69 in women. In both groups, people with an FI greater than or equal to the 99% limit showed close to 100% mortality by 5 years. CONCLUSION: Compared with men, women appeared to better tolerate deficits in health, yielding both relatively lower mortality and higher limit values to the FI. Even so, the FI did not exceed 0.7 in any individual.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".