ASSOCIATION OF THE DRUG BURDEN INDEX WITH BALANCE IMPAIRMENT AND RECENT FALLS IN COMMUNITY- DWELLING OLDER ADULTS
Bibliographic record
Abstract
Medications with anticholinergic and sedative properties, even when used in isolation, can negatively impact the physical performance of older adults. Managing complex pharmacological regimens in potentially multimorbid patients remains a significant challenge. This study aimed to examine the association between medication burden, as measured by the Drug Burden Index (DBI), and both postural balance and recent falls in community-dwelling older adults. Medication use was assessed through a structured questionnaire covering both prescribed and over-the-counter drugs taken chronically. The DBI was then calculated. Postural balance was evaluated using a BIOMEC400 force platform during a one-legged stance, and fall events over the previous six months were recorded. The sample consisted of 179 participants from a university outpatient clinic; 70.8% were female and 60.0% were white. A total of 75 participants (41.9%) had a low or high medication burden. These individuals demonstrated significantly worse balance across all parameters (Kruskal–Wallis test, p < 0.05). Additionally, medication burden was strongly associated with recent falls (Chi-square test for trend = 34.1, p < 0.0001). ROC curve analysis identified a DBI cut-off point of 0.5 as being associated with impaired balance. In conclusion, a DBI above 0.5 appears to increase the risk of balance impairment and falls in older adults. The DBI may therefore be a useful public health tool to identify high-risk individuals and support safer medication use in this population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".