269 Early Identification of Frailty: Developing an International Delphi Consensus for a Definition of Pre-frailty
Bibliographic record
Abstract
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.
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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 | 0.276 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".