The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Agreement Between 35 Published Frailty Scores in the General Population
Open accessAbstract
In elderly populations, frailty is associated with higher mortality risk. Although many frailty scores (FS) have been proposed, no single score is considered the gold standard. We aimed to evaluate the agreement between a wide range of FS in the English Longitudinal Study of Ageing (ELSA). Through a literature search, we identified 35 FS that could be calculated in ELSA wave 2 (2004-2005). We examined agreement between each frailty score and the mean of 35 FS, using a modified Bland-Altman model and Cohen's kappa (κ). Missing data were imputed. Data from 5,377 participants (ages ≥60 years) were analyzed (44.7% men, 55.3% women). FS showed widely differing degrees of agreement with the mean of all scores and between each pair of scores. Frailty classification also showed a very wide range of agreement (Cohen's κ = 0.10-0.83). Agreement was highest among "accumulation of deficits"-type FS, while accuracy was highest for multidimensional FS. There is marked heterogeneity in the degree to which various FS estimate frailty and in the identification of particular individuals as frail. Different FS are based on different concepts of frailty, and most pairs cannot be assumed to be interchangeable. Research results based on different FS cannot be compared or pooled.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
Frailty in Older Adults: Evidence for a Phenotype
Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., Seeman, T., Tracy, R., Kop, W. J., Burke, G., et al. (2001) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/56.3.m146 · Cited by 24,907
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J. M., & Wouters, E. J. M. (2016) · Gerontology · doi:10.1159/000430949 · Cited by 19,690
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Guralnik, J. M., Simonsick, E. M., Ferrucci, L., Glynn, R. J., Berkman, L. F., Blazer, D. G., Scherr, P. A., & Wallace, R. B. (1994) · Journal of Gerontology · doi:10.1093/geronj/49.2.m85 · Cited by 9,792
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
A global clinical measure of fitness and frailty in elderly people
Rockwood, K. (2005) · Canadian Medical Association Journal · doi:10.1503/cmaj.050051 · Cited by 8,968
(2010) · University of Twente Research Information · Cited by 6,037
Cited by
The Continuum of Aging and Age-Related Diseases: Common Mechanisms but Different Rates
Franceschi, C., Garagnani, P., Morsiani, C., Conte, M., Santoro, A., Grignolio, A., Monti, D., Capri, M., & Salvioli, S. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00061 · Cited by 945
Outcomes of hospital admissions among frail older people: a 2-year cohort study
Keeble, E., Roberts, H. C., Williams, C. D., Van Oppen, J., & Conroy, S. P. (2019) · British Journal of General Practice · doi:10.3399/bjgp19x704621 · Cited by 132
Wu, C., Geldhof, G. J., Xue, Q.-L., Kim, D. H., Newman, A. B., & Odden, M. C. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy041 · Cited by 45
Metadata
- Authors
- Aguayo, G. A., Donneau, A.-F., Vaillant, M. T., Schritz, A., Franco, O. H., Stranges, S., Malisoux, L., Guillaume, M., & Witte, D. R.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-06-15
- Volume
- 186
- Issue
- 4
- Pages
- 420-434
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2625596992
- Cited by
- 264
- References
- 57
- FWCI
- 12.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Agreement Between 35 Published Frailty Scores in the General Population. https://app.cochat.ai/papers/10.1093/aje/kwx061
- The paper
- Aguayo, G. A., Donneau, A.-F., Vaillant, M. T., Schritz, A., Franco, O. H., Stranges, S., Malisoux, L., Guillaume, M., & Witte, D. R. (2017). Agreement Between 35 Published Frailty Scores in the General Population. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwx061
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
