Use of fall risk increasing drugs in residents of retirement villages: a pilot study of long term care and retirement home residents in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Falls continue to be a problem for older people in long-term care (LTC) and retirement home (RH) settings and are associated with significant morbidity and health care use. Fall-risk increasing drugs (FRIDs) are known to increase fall risk and represent modifiable risk factors. There are limited data regarding the use of FRIDs in contemporary LTC and RH settings, and it has not been well documented to what extent medication regimens are reviewed and modified for those who have sustained falls. The objective of this study is to characterize medication related fall risk factors in LTC and RH residents and on-going use of medications known to increase fall risk. METHODS: Retrospective chart review of residents aged >65 who sustained one or more falls living in LTC or RH settings. RESULTS: 105 residents who fell one or more times during 2009-2010 were identified with a mean age of 89 years, a mean of nine scheduled medications and seven diagnoses, and 83% were women. Residents in LTC were ostensibly at higher risk for falls relative to those in RH settings as suggested by higher proportion of residents with multiple falls, multiple comorbidities, comorbidities that increase fall risk and visual impairment. Post fall injuries were sustained by 42% of residents, and residents in RH sustained more injuries relative to LTC residents (47 vs 34%). Use of FRIDs such as benzodiazepines, antipsychotic, antidepressant and various antihypertensive drugs was common in the present sample. No medication regimen changes were noted in the 6-month post fall period. CONCLUSIONS: The present study documented common use FRIDs by LTC and RH residents with multiple falls. These potentially modifiable falls risk factors are not being adequately addressed in contemporary practice, demonstrating that there is much room for improvement with regards to the safe and appropriate use of medications in LTC and RH residents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".