The association between delirium and falls in older adults in the community: a systematic review
Bibliographic record
Abstract
ABSTRACT Objective Systematically review and critically appraise evidence for the association between delirium and falls in community-dwelling adults aged 60 years and above Methods We searched EMBASE, MEDLINE, PsycINFO, Cochrane Database of Systematic Reviews, CINAHL and Evidence-Based Medicine Reviews (EBMR) databases in April 2023. Standard methods were used to screen, extract data, assess risk of bias (using Newcastle Ottawa scale), provide a narrative synthesis and where appropriate conduct meta-analysis. Results We included eight studies, with at least 3505 unique participants. Five found limited evidence for an association between delirium and subsequent falls: one adjusted study showed an increase in falls (RR 6.66;95% CI 2.16-20.53) but the evidence was low certainty. Four non-adjusted studies found no clear effect. Three studies (one with two subgroups treated separately) found some evidence for an association between falls and subsequent delirium: meta-analysis of three adjusted studies showed an increase in delirium (pooled OR 2.01; 95%CI 1.52-2.66), one subgroup of non-adjusted data found no clear effect. Number of falls and fallers were reported in the studies. Four studies and one subgroup were at high risk of bias and one study had some concerns. Conclusions We found limited evidence for the association between delirium and falls. More methodologically rigorous research is needed to understand the complex relationship, establish how and why this operates bi-directionally and identify potential modifying factors involved. We recommend the use of standardised assessment measures for delirium and falls. Clinicians should be aware of the potential relationship between these common presentations. Key points This is the first systematic review of the association between delirium and falls in the wider community population. There is relatively limited but consistent evidence on the direction of effect for both delirium preceding falls and falls preceding delirium. More high-quality longitudinal work is needed to explore the nature of this potentially complex and bidirectional relationship. History of falls and delirium should be considered when assessing patients with incidence/suspected incidence of falls or delirium.
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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 | 0.022 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".