Fall-related Injuries in Older Adults and Medications Prescribed within 30 Days Prior to the Injury
Bibliographic record
Abstract
Abstract Fall-related injuries in older adults have serious consequences both for individuals and the public health care system. The purpose of this study was to identify medication classes prescribed within 30 days prior to the injury that were associated with fall-related injuries in older adults. This population-based, case-control study used secondary administrative health care data in Ontario, Canada. The cases were older adults, aged 66 years and older, who visited an emergency department for a fall-related injury. Controls were extracted from the Registered Person Database, and matched by same age, sex and residence area. Medication classes prescribed to both groups were recorded and logistic regression was conducted to examine the association between medications and fall-related injury. The case group included 255,270 older adults who experienced a fall-related injury over the five-year period (2010-2014). After adjustment for sex, age group, residence area, income level and number of medications prescribed, psychotropic medications (i.e., opioids, anti-epileptics, anti-Parkinson’s drugs, and antidepressants), drugs for treatment of constipation, infection and benign prostatic hyperplasia, antithrombotic agents, statins and bronchodilators were identified to be related to increased risk of fall-related injuries. In addition to medications already on the list of fall-risk increasing drugs or FRIDs, this study uncovered that drugs for benign prostatic hyperplasia, cephalosporins, biphosphates and bronchodilators increased the risk of fall-related injury in older adults. Well-designed prospective cohort studies considering prescription indication and drug-drug interactions are needed to provide more convincing evidence on medications that may be associated with increased risks of fall-related injury in older adults.
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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 | 0.002 |
| 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) | 0.002 | 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".