Comparison of CAROC and FRAX in Fragility Fracture Patients: Agreement, Clinical Utility, and Implications for Clinical Practice
Bibliographic record
Abstract
OBJECTIVE: To examine the level of agreement between 2 fracture risk assessment tools [Canadian Association of Radiologists and Osteoporosis Canada (CAROC) and Canadian Fracture Risk Assessment (FRAX)] when applied within the context of the Canadian guidelines, in a population of fragility fracture patients. METHODS: The sample consisted of 135 treatment-naive fragility fracture patients aged 50+ years and screened as part of an osteoporosis (OP) program at an urban hospital. Ten-year probabilities of future major osteoporotic fractures were calculated using the FRAX and CAROC. We also integrated additional qualifiers from the 2010 Canadian guidelines that place hip, spine, and multiple fractures at high risk regardless. A quadratic weighted κ (Kw) and 95% CI were calculated to estimate the chance corrected agreement between the risk assessment tools. Logistic regression was used to evaluate the factors associated with concordance. RESULTS: Among patients with fragility fractures, the agreement between CAROC and FRAX was Kw = 0.64 (95% CI 0.58-0.71), with 45 of 135 cases in the cells reflecting disagreement. Younger persons and males were more likely to be found in discordant cells. CONCLUSION: The level of agreement between 2 commonly used fracture risk assessment tools was not as high in the patients with fragility fractures as it was in general community-based samples. Our results suggest discordance is found in less-typical patients with OP who need more consistency in messaging and direction. Users of these fracture risk tools should be aware of the potential for discordance and note differences in risk classifications that may affect treatment decisions.
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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 | 0.027 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".