Mortality prediction of 35 frailty scores in a 7-years follow-up study in elderly general population
Bibliographic record
Abstract
Background Frailty is a state of vulnerability in elderly people, which is associated with a higher risk of mortality. Many frailty scores (FS) have been developed, but none of them is considered the gold standard. We aimed to compare the predictive ability of a list of FS with regard to total mortality in a general population sample of elderly in England. Methods We performed a prospective analysis of the association between 35 FS calculated in wave 2 of the English Longitudinal Study of Ageing (2004-2005) and all-cause mortality assessed up to 2012. The 35 FS were rescaled to the range 0 (no frailty) to 1 (frailty). Hazard ratios (HR) and corresponding 95% confidence intervals (95% CI) were calculated for each FS using Cox proportional hazards model. The added discriminative ability was studied with Harrell’s C statistic (HC) as well as the net reclassification index (NRI). Results Data from 5,294 participants (44.9% men) were analysed. The mean age was 71.2 (SD: ± 8.0) years. The prevalence of cardiovascular disease and cancer was 13.7% and 9.3% respectively. The median follow-up was 7.1 years and the mortality rate was 326/10,000 person-years, with an overall number of 1144 deaths out of the 5,294 participants. In fully adjusted models with socio-demographic, lifestyle and comorbidity items, HR ranged from: 9.3 (95% CI: 5.6; 15.4) to 1.5 (95% CI: 1.0; 2.2). Delta HC ranged from 1.2% (95% CI: 0.7; 1.6) to 0% (95% CI: -0.1; 0.1) of improvement. The continuous NRI ranged from 0.02 (0.012; 0.029) to 0 (0; 0.002). Conclusions There is high variability in the association between FS and 7-year mortality. The FS most strongly related to mortality were the G8-Geriatric Screening Tool and the Edmonton Frail Scale. Although all FS show associations with mortality, their added discriminative ability seems modest. Our results will help to guide clinicians, researchers and public health practitioners in choosing the most informative instrument. Key messages: While some FS are very strong predictors of all-cause mortality, however there is large variation between different instruments widely used in the literature Although the mortality prediction of some frailty scores is high, their ability to separate participants who die or survive within a full-adjusted model is limited
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".