SEX DIFFERENCES AND LONGITUDINAL QUANTILES OF FRAILTY TRAJECTORIES WITH MISSING DATA DUE TO DEATH
Bibliographic record
Abstract
Abstract Scarce evidence exists about frailty trajectories, but the evidence suggests that women live longer with higher levels of frailty. When progression of frailty was studied, the focus has been on mean trajectories and research has ignored death. Here, we aim to assess the role of sex, age, and education in different quantiles of the distribution of frailty trajectories. We derived a frailty index (FI, range 0–1) based on the accumulation of deficits in individuals aged 65 at baseline (n=6929) using data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). We applied weighted Generalized Estimating Equations (weighted GEE) to adjust the quantiles of the FI trajectory by sex, and education. The results show that the median FI trajectory increases with age (b_age0.5=0.008, p < 0.001) and this increase is higher for women than men (b_age*sex0.5=0.003, p < 0.001), but sex differences disappear for the most frail (0.9 percentile) once the missing data process is accounted for (b_age*sex0.9=0.002, p=0.085). For the most frail the increase with age is higher than for those at the median (b_age0.9=0.018, p < 0.001; test diff, p < 0.001) and education reduces the progression of the median FI (b_age*edu0.5=-0.0003, p < 0.001). Our approach advances the understanding of frailty trajectories of older adults, showing differences across quantiles. Thus, this may improve the practice and design of interventions aimed at older adults. In addition, we address the process of missing information and the results show new insights, particularly for those who are most frail.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".