Population-based Studies on Medications and Fall-related Injury in Older Adults
Bibliographic record
Abstract
Background: Fall-related injuries in older adults result in serious consequences to individuals and health care system, especially with the increasing aging population. The purpose of this study was to (1) describe medication prescription patterns within one year prior to fall-related injuries; (2) identify medication classes prescribed within 30 days prior to the injury that were associated to fall-related injury; and (3) examine the association between fall-related injuries and continuous use or new initiation of most commonly prescribed medications.\nMethods: Studies used administrative health care data in Ontario. Study 1 described the frequency of medications prescribed to older adults within one year before they had fall-related injuries. Study 2 and 3 were case-control studies. The cases were older adults aged 66 years and older, who had a fall-related injury between January 2010 and December 2014. Controls were older adults with same age, sex and residence area as the cases. In study 2, medications prescribed to both groups were recorded and logistic regression was conducted to examine the association between medications and injuries. Study 3 defined continuous use as medication use for more than 90 days and new initiation was defined as starting a medication within 30 days prior to injuries. Logistic regression was conducted to examine the association between injuries and continuous use or new initiation of medications.\nResults: Within one year before the injury, 27.2% of older adults were prescribed antidepressants, 25.0% opioids, 16.6% anxiolytics, and 36.4% were prescribed 5-9 medications and 41.2% were prescribed 10 or more medications. After adjustment for sex, age group, residence area, income and number of medications prescribed, laxatives, antibiotics and bronchodilators were identified to increase the risk of fall-related injury. Continuous use of antidepressants, anticholinesterases and antithrombin agents and new initiation of antidepressants, opioids and cephalosporins were reported to increase the risk for injuries.\nConclusion: Findings of this thesis uncovered several medication classes such as antibiotics and bronchodilators were associated with increased risk of fall-related injury. Both, continuous use and new initiation of particular medication classes were associated with injuries. Well-designed prospective cohort studies are needed to provide more convincing evidence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".