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 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 | 0.045 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".