Medication Prescribed Within 1 Year Preceding Fall-Related Injuries in Older Adults in Ontario, Canada
Bibliographic record
Abstract
Abstract Background: The consequences of fall-related injuries are becoming more significant due to ageing societies worldwide. This study aims to provide information on medications prescribed to older adults within one year before they experienced fall-related injury in Ontario, Canada. Methods: A population-based descriptive study of older adults (66 years and older) who experienced fall-related injury was conducted using administrative secondary health care data of Ontario. The percentages of patients prescribed each Anatomical Therapeutic Chemical 4th level medication class and fall-risk increasing drugs one year before their fall-related injuries was summarized. Results: From 2010 to 2014, 288,251 older adults (63.2% females) were admitted to Emergency Department due to fall-related injury, 39.9% were fall-related fractures, 12.6% were head injuries. One year prior to their injury, 48.46% of older adults were prescribed with statins; 35.23% were prescribed with diuretics; 26.84% were prescribed with antidepressants; 25.90% were prescribed with opioids and 16.61% were prescribed with anxiolytics. A higher percentage of females were prescribed with diuretics, antidepressants, and anxiolytics than males. 85 years and older people had higher percentage of prescription of diuretics, antidepressants and antipsychotics than other age group. Discussion: In general, older adults diagnosed with fall-related injuries were prescribed with more opioids, benzodiazepines and antidepressants than other general older adults. There were distinct patterns of prescription medication within each sex and age group (66-74 group, 75-84 group and 85 years and older group). Further association between medications and fall-related injuries need to be established using 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".