Medication Prescribed Within One Year Preceding Fall-Related Injuries in Ontario Older Adults
Bibliographic record
Abstract
Background: Serious injuries secondary to falls are becoming more prevalent due to the worldwide ageing of societies. Several medication classes have been associated with falls and fall-related injuries. The purpose of this study was to describe medication classes and the number of medication classes prescribed to older adults prior to the fall-related injury. Methods: level medication classes. Frequency of medications prescribed to older adults was calculated on different sex, age groups, types of medications, and injures. Results: Over five years (2010-2014), 288,251 older adults (63.2% females) were admitted to an emergency department for a fall-related injury (40.0% fractures, 12.1% brain injury). In the year before the injury, 48.5% were prescribed statins, 27.2% antidepressants, 25.0% opioids, and 16.6% anxiolytics. Females were prescribed more diuretics, antidepressants, and anxiolytics than males; and people aged 85 years and older had a higher percentage of diuretics, antidepressants, and antipsychotics. There were 36.4% of older adults prescribed 5-9 different medication classes and 41.2% were prescribed 10 or more medication classes. Discussion: Older adults experiencing fall-related injuries were prescribed more opioids, benzodiazepines, and antidepressants than previously reported for the general population of older adults in Ontario. Higher percentage of females and more 85+ older adults were prescribed with psychotropic drugs, and they were also found to be at higher risk of fall-related injuries. Further associations between medications and fall-related injuries need to be explored in well-defined cohort studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".