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Record W2973485702 · doi:10.1093/ageing/afz103.168

269 Early Identification of Frailty: Developing an International Delphi Consensus for a Definition of Pre-frailty

2019· article· en· W2973485702 on OpenAlexaff
Duygu Sezgin, Mark O’Donovan, Jean Wong, Karen Bandeen‐Roche, Giuseppe Liotta, Nicola Fairhall, Ángel Rodríguez‐Laso, João Apóstolo, Roger Clarnette, Carol Holland, Regina Roller‐Wirnsberger, Maddalena Illario, Leocadio Rodríguez‐Mañas, Miriam Vollenbroek-Hutten, Burcu Balam Doğu, Cafer Balcı, Constança Paúl, Emer Ahern, Román Romero‐Ortuño, William Molloy, Diarmuid O’Shea, John Cooke, Deirdre Lang, Anne Hendry, Kenneth Rockwood, Andrew Clegg, Aaron Liew, Rónán Ó’Caoimh

Bibliographic record

VenueAge and Ageing · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDelphi methodMedicineIdentification (biology)Psychological interventionDelphiQuality of life (healthcare)GerontologyNursingComputer science

Abstract

fetched live from OpenAlex

Abstract Background Frailty is associated with a prodromal stage called pre-frailty, a potentially reversible and highly prevalent condition before frailty becomes established. Despite this, there is no widely accepted definition of pre-frailty to support its early identification and management. This study applied an international consensus approach to define and better understand pre-frailty. Methods A modified electronic two-round Delphi Consensus study was conducted. In all, 23 experts from 12 countries with different backgrounds participated. The questionnaire was developed following a systematic literature review. An online consensus meeting was conducted with eight Delphi participants and two external experts. Qualitative and quantitative methods were employed for data analysis. An agreement level of 70% was applied for accepting statements. Results A total of 71 statements were circulated in Round 1. Of these, 52.8% were accepted. Fifty-one statements were re-circulated in Round 2, of which 92.1% were accepted. The online consensus meeting produced a consensus statement describing the concept, multi-factorial nature, and mechanism of pre-frailty as well as assessment, prevention and management approaches. All experts agreed that physical and non-physical factors such as psychological and social capacity are involved in the development of pre-frailty, potentially adversely affecting health and health-related quality of life outcomes. Practitioners should regard pre-frailty as a multi-factorial, multi-dimensional, and non-linear process that does not inevitably lead to frailty. It might be reversed or attenuated by targeted interventions. Brief, feasible and validated tools are recommended for opportunistic screening or case-finding followed by confirmation with multi-dimensional assessment. Conclusion It is difficult to establish consensus on one compact definition of pre-frailty, which is a multi-dimensional concept not only associated with physical impairment, but also with cognitive, nutritional, socioeconomic and other aspects of frailty. However, it may be too early to agree on an operational definition of pre-frailty since none yet exists for frailty.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.178
GPT teacher head0.437
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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