Comparison of FRAX score to bone mineral density for estimating fracture risk in patients with CRPC on androgen-deprivation therapy (ADT).
Bibliographic record
Abstract
101 Background: A FRAX algorithm has been elaborated to estimate the ten-year hip fracture risk associated with this under-diagnosed condition. We aim to evaluate the fracture risk of patients who would otherwise be left untreated by the conventional T-score. Methods: Clinical data from 613 PCa patients undergoing ADT was collected from our AMP large urology group. Fracture risk was assessed using the country specific (USA) Fracture Risk Assessment Tool (FRAX). Also, a subset of patients (n=94) had received Dual-energy X-ray Absorptiometry (DXA). We compared the proportion of patients suitable for treatment according to the threshold of the FRAX fracture risk calculated with the BMD (>3%) and the T-score (<-2.5). Results: According to the FRAX algorithm (without BMD), 61.6% of our cohort require treatment. The FRAX score (with BMD) identified 46.8% of patients who had DXA suitable for treatment, in contrast to 19.1% by the T-score alone. Correlations were calculated between the various methods (Table). Conclusions: Our results demonstrate that many patients unidentified for treatment by the conventional T-score are at significant risk for fracture according to the FRAX algorithm with BMD. When calculated without the BMD, an even greater proportion of patients is found to be at risk and suitable for treatment. [Table: see text]
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".