Fracture Risk Prediction Using the Fracture Risk Assessment Tool in Individuals With Cancer
Bibliographic record
Abstract
Importance: The Fracture Risk Assessment Tool (FRAX) is a fracture risk prediction tool for 10-year probability of major osteoporotic fracture (MOF) and hip fracture in the general population. Whether FRAX is useful in individuals with cancer is uncertain. Objective: To determine the performance of FRAX for predicting incident fractures in individuals with cancer. Design, Setting, and Participants: This retrospective population-based cohort study included residents of Manitoba, Canada, with and without cancer diagnoses from 1987 to 2014. Diagnoses were identified through the Manitoba Cancer Registry. Incident fractures to March 31, 2021, were identified in population-based health care data. Data analysis occurred between January and March 2023. Main Outcomes and Measures: FRAX scores were computed for those with bone mineral density (BMD) results that were recorded in the Manitoba BMD Registry. Results: This study included 9877 individuals with cancer (mean [SD] age, 67.1 [11.2] years; 8693 [88.0%] female) and 45 877 individuals in the noncancer cohort (mean [SD] age, 66.2 [10.2] years; 41 656 [90.8%] female). Compared to individuals without cancer, those with cancer had higher rates of incident MOF (14.5 vs 12.9 per 1000 person-years; P < .001) and hip fracture (4.2 vs 3.5 per 1000 person-years; P = .002). In the cancer cohort, FRAX with BMD results were associated with incident MOF (HR per SD increase, 1.84 [95% CI, 1.74-1.95]) and hip fracture (HR per SD increase, 3.61 [95% CI, 3.13-4.15]). In the cancer cohort, calibration slopes for FRAX with BMD were 1.03 for MOFs and 0.97 for hip fractures. Conclusions and Relevance: In this retrospective cohort study, FRAX with BMD showed good stratification and calibration for predicting incident fractures in patients with cancer. These results suggest that FRAX with BMD can be a reliable tool for predicting incident fractures in individuals with cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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; 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".