Interventions for cognitive frailty: developing a Delphi consensus with multidisciplinary and multisectoral experts
Bibliographic record
Abstract
Introduction: The conjunction of physical frailty and cognitive impairment without dementia is described as Cognitive Frailty (CF). Indications that CF is potentially reversible have led to proposals that risk factors, symptoms or mechanisms of CF would be appropriate targets for interventions for prevention, delay or reversal. However, no study has brought experts together across sectors to determine targets, content or mode of interventions, and most resources on interventions are from the perspective of academic or clinical researchers only. This international Delphi consensus study brings together experts from academic and clinical research, lay people with lived experience of CF, informal carers, and professional care practitioners/clinicians. Methods: Three rounds of Delphi study were held to discern which factors and statements were agreed upon by the whole sample and which generated different views in those with differing expertise. A scoping review and Round 1 (29 participants) were used to gather initial statements. In Round 2, 58 people responded to statements and open text items, comprising 7 lab-based researchers, 27 researchers working with people, 14 people with lived experience or informal family carers, and 10 professional carers/clinicians. Percent agreement and qualitative responses were analyzed to provide a final set of statements which were checked by 38 respondents in Round 3. Results: Analysis of Round 2 quantitative data provided 74 statements on which there was at least 70% agreement and qualitative data produced a further 24 statements. These were combined to provide 90 statements for Round 3. There was Consensus for 89 of the statements. A few differences between the groups were observed at both stages. Discussion and conclusion: The consensus for statements associated with CF interventions provides a useful first step in defining health promotion activities and interventions. Given the prevalence and potential disability caused by CF in older populations, the consensus statements represent expert opinion that is inter-sectoral and will inform public health policies to support implementation of evidence-based prevention and intervention plans. This study is an important step toward changing current approaches, by including all stakeholders from the outset. Outcomes can be used to feed into co-creation of interventions for cognitive 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 | 0.182 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".