45FRACTURE RISK PREDICTION AND TREATMENT THRESHOLDS USING FRAX, GARVAN AND QFRACTURE IN AN OSTEOPOROSIS CLINIC POPULATION
Bibliographic record
Abstract
Introduction: Fracture prediction tools are increasingly used in osteoporosis treatment decisions. FRAX, Garvan and QFracture are the most common web based ones used. It is unknown if these tools would identify the same patients for treatment. The aim of this study was to compare these tools with each other in individual patients. Method: We applied these 3 tools to women consecutively attending osteoporosis clinics and calculated their 10 year major osteoporotic and hip fracture risk, utilising a 20% intervention threshold to determine treatment. We also compared the 20% intervention threshold of FRAX (FRAX-20) with National Osteoporosis Guideline Group (NOGG) guidelines (age related threshold). Results: 100 women (mean age = 70.1; SD = 11.3 years) were studied. Compared with FRAX, Garvan overestimated major osteoporotic fracture risk by 2-fold [41.5%(95%CI = 31.8-51.2) versus 18.8%(95%CI = 11.1-26.5)], and hip fracture by nearly 4-fold [24.7%(95%CI = 16.2-33.2) versus 7.1%(95%CI = 2.1-12.1)]. With QFracture and FRAX the risk of major osteoporotic fracture was similar [22.4%(95%CI = 14.2-30.6) versus 18.8%(95%CI = 11.1-26.5)], while hip fracture risk was twice as high [16.8%(95%CI = 9.5-24.1) versus 7.1%(95%CI = 2.1-12.1)]. FRAX-20, Garvan and QFracture recommended treatment in 38%, 75% and 40% respectively. There was discordance, with 26% of patients being recommended for treatment by all 3 tools, and 24% not recommended treatment by all tools. FRAX-20 would treat 12 patients that NOGG would not treat, and NOGG would treat 20 patients that FRAX-20 would not treat.
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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 | 0.000 | 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 | 0.000 |
| 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".