Sustainable fall prevention across Europe: challenges and opportunities
Bibliographic record
Abstract
Falls and related injuries form a growing health-care problem in aging societies. Between 40 and 60% of older fallers in the last year report being injured. Around 15–20% of falls result in serious (non-fracture) injuries including fractures. Non-injurious falls have also been associated with adverse health effects, including accelerated functional decline, anxiety and depression, fear of falling, and social withdrawal. Consequently, fall incidents have an impact on societal health-care expenditure, equaling 0.85–1.5% of the total health-care expenditure in Western countries. To tackle this health-care issue, many countries with developed health-care services have established fall prevention services. Given its multifactorial nature, it is assumed that comprehensive geriatric assessment (CGA) leading to individually targeted interventions would be effective. Previous literature has shown that several good quality trials have resulted in a reduction in falls. Despite local differences, these services generally address risk stratification and multifactorial assessment (MA) of risk factors and accompanying interventions. Accordingly, several medical societies and organizations have published clinical practice guidelines for fall prevention and management and a recent systematic review found a high degree of agreement in several areas. Successful implementation of health-care intervention such as fall prevention depends on many factors at different health-care levels including the innovation, individual professional, patient, social context, organizational context, and the economic and political context. For successful and durable implementation of fall services, collaboration between relevant medical disciplines, health-care insurers and governmental bodies is essential. To facilitate this, the Special Interest Group on Falls and Fracture prevention of the European Geriatric Medicine Society is currently preparing an international survey on current practices in fall prevention services throughout Europe to determine gaps and opportunities, identify best practices, and relevant stakeholders for sustainable fall prevention for older persons. Also, to achieve global consensus on the optimal content of fall preventive interventions and to facilitate knowledge distribution, the above-mentioned task force of worldwide experts was installed in 2019 at the first World Falls and Postural Stability Conference in Kuala Lumpur, Malaysia, to produce the first World Falls Prevention and Management Guidelines. The guidelines will include overall recommendations and more specific ones with regard to assessment, risk stratification, and interventions in different settings and risk groups.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".