Interventions to prevent, delay or reverse frailty in older people: a journey towards clinical guidelines
Bibliographic record
Abstract
BACKGROUND: Age-related frailty is a multidimensional dynamic condition associated with adverse patient outcomes and high costs for health systems. Several interventions have been proposed to tackle frailty. This correspondence article describes the journey through the development of evidence- and consensus-based guidelines on interventions aimed at preventing, delaying or reversing frailty in the context of the FOCUS (Frailty Management Optimisation through EIP-AHA Commitments and Utilisation of Stakeholders Input) project (664367-FOCUS-HP-PJ-2014). The rationale, framework, processes and content of the guidelines are described. MAIN TEXT: The guidelines were framed into four questions - one general and three on specific groups of interventions - all including frailty as the primary outcome of interest. Quantitative and qualitative studies and reviews conducted in the context of the FOCUS project represented the evidence base. We followed the GRADE Evidence-to-Decision frameworks based on assessment of whether the problem is a priority, the magnitude of the desirable and undesirable effects, the certainty of the evidence, stakeholders' values, the balance between desirable and undesirable effects, the resource use, and other factors like acceptability and feasibility. Experts in the FOCUS consortium acted as panellists in the consensus process. Overall, we eventually recommended interventions intended to affect frailty as well as its course and related outcomes. Specifically, we recommended (1) physical activity programmes or nutritional interventions or a combination of both; (2) interventions based on tailored care and/or geriatric evaluation and management; and (3) interventions based on cognitive training (alone or in combination with exercise and nutritional supplementation). The panel did not support interventions based on hormone treatments or problem-solving therapy. However, all our recommendations were weak (provisional) due to the limited available evidence and based on heterogeneous studies of limited quality. Furthermore, they are conditional to the consideration of participant-, organisational- and contextual/cultural-related facilitators or barriers. There is insufficient evidence in favour of or against other types of interventions. CONCLUSIONS: We provided guidelines based on quantitative and qualitative evidence, adopting methodological standards, and integrating relevant stakeholders' inputs and perspectives. We identified the need for further studies of a higher methodological quality to explore interventions with the potential to affect 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 | 0.128 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".