European position paper on polypharmacy and fall-risk-increasing drugs recommendations in the World Guidelines for Falls Prevention and Management: implications and implementation
Bibliographic record
Abstract
Falls prevention and management in older adults is a critical global challenge. One of the key risk factors for falls is the use of certain medications. Therefore, to prevent medication-related falls, the following is recommended in the recent World Guidelines for Falls Prevention and Management: (1) assess for fall history and the risk of falls before prescribing potential fall-risk-increasing drugs (FRIDs), (2) use a validated, structured screening and assessment tool to identify FRIDs when performing a medication review, (3) include medication review and appropriate deprescribing of FRIDs as a part of the multifactorial falls prevention intervention, and (4) in long-term care residents, if multifactorial intervention cannot be conducted due to limited resources, the falls prevention strategy should still always include deprescribing of FRIDs.In the present statement paper, the working group on medication-related falls of the World Guidelines for Falls Prevention and Management, in collaboration with the European Geriatric Medicine Society (EuGMS) Task and Finish group on FRIDs, outlines its position on how to implement and execute these recommendations in clinical practice.Preferably, the medication review should be conducted as part of a comprehensive geriatric assessment to produce a personalized and patient-centered assessment. Furthermore, the major pitfall of the published intervention studies so far is the suboptimal implementation of medication review and deprescribing. For the future, it is important to focus on gaining which elements determine successful implementation and apply the concepts of implementation science to decrease the gap between research and practice.
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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 | 0.040 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".