Machine learning approaches and risk factors for fall risk prediction in Canadian community-dwelling older adults: A scoping review protocol
Bibliographic record
Abstract
Machine learning is a subset of artificial intelligence that is emerging as a powerful tool in healthcare research for analyzing complex datasets and identifying nonlinear relationships that traditional statistical methods might overlook (Ostojic et al., 2024). Falls among older adults are a worldwide public health concern (WHO, 2021) and are the leading cause of injury in Canada’s aging population (Public Health Agency of Canada, 2014). Machine learning approaches to predict fall risk offer new perspectives on a longstanding problem but remain limited in studies involving Canadian community-dwelling older adults. In an era of open-access information and the availability of large health datasets that include questions about falls, it is crucial to use these resources to understand the factors that predict fall risk. This scoping review aims to identify existing machine learning approaches to predict fall risk among Canadian community-dwelling older adults using self-reported survey data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.001 | 0.011 |
| 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; both teacher heads agree on what is shown here.
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".