Next Generation Imminent Fracture Risk Assessment Using AI
Bibliographic record
Abstract
Abstract Osteoporosis-related fractures are a significant cause of morbidity and loss of independence, particularly in older adults. While traditional tools like FRAX predict long-term fracture risks, they lack precision in identifying Imminent Fracture Risk , defined as the risk of fractures within a two-year period following an initial incident. This study develops a machine learning model that integrates demographic and clinical factors to address this gap, aiming to improve short-term fracture risk predictions and enable timely interventions. The model was trained on a dataset of 32,677 patients from the Ontario Osteoporosis Strategy’s Fracture Screening and Prevention Program. It leverages an ensemble learning framework that combines Support Vector Machine, Decision Tree, Logistic Regression, and AdaBoost classifiers. Using GridSearchCV for hyperparameter tuning, the model achieved an accuracy of 76%, a recall of 76%, and an AUROC of 0.73, highlighting its potential for clinical application. Despite these promising results, limitations such as the absence of Bone Mineral Density data and incomplete patient-reported information restricted the model’s generalizability. Future research should focus on expanding the dataset, incorporating real-time data from wearable devices, and utilizing advanced natural language processing techniques to handle unstructured data effectively. This study demonstrates the potential of machine learning in predicting imminent fracture risk, offering a complementary tool to traditional methods like FRAX. By improving the early identification of high-risk individuals, this approach could significantly reduce both personal and economic burdens associated with osteoporotic fractures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".