A combined screening approach of Fracture (Fx) Risk Algorithm (FRAX) and Trabecular Bone Score (TBS) to identify osteoporotic-range fracture risk (ORFR) in breast cancer (BC) patients treated with adjuvant aromatase inhibitor (AI).
Bibliographic record
Abstract
574 Background: The NCCN recommends serial bone mineral density (BMD) measurement with dual energy x-ray absorptiometry (DXA) to diagnose and treat AI-associated osteoporosis. The FRAX algorithm identifies additional patients with ORFR who may benefit from anti-resorptive therapy (ART). The TBS, which measures bone microstructure by DXA, is an independent indicator of ORFR. Here, we retrospectively evaluate the utility of a combined screening approach (BMD+FRAX+TBS) in identifying ORFR at baseline and following AI. Methods: Breast cancer patients > 60 years, treated with AI and no ART between 2006-12, who had serial DXA at Memorial Sloan Kettering Cancer Center were identified (n= 74). BMD, FRAX, and TBS were evaluated at baseline (< 3 months from AI initiation) and at 12-24 months, and various screening strategies for identifying ORFR were assessed. Based on National Osteoporosis Foundation criteria and Manitoba TBS study fracture rates, ORFR was defined as: BMD T-score≤-2.5; ≥3% hip or ≥20% osteoporosis-associated 10-year fracture risk by FRAX; or TBS score≤1.2 with BMD T-score< -1.0. Results: Following AI, lumbar spine (LS)-BMD declined in 75% of patients (median: -2.9%; SD: 4.3%) and TBS declined in 58% of patients (median: -1.0%, SD: 7.7%). Declines in LS-BMD and TBS were not correlated (Spearman r=-.16, p=NS) and were not influenced by age, BMI, ethnicity, or chemotherapy (by Wilcoxon rank-sum). Compared to BMD alone, a combined screening approach (BMD+FRAX+TBS) identified an additional 15% of patients with ORFR at baseline. (table) Following AI, an additional 2% developed ORFR by BMD alone, versus 7% by BMD+FRAX+TBS. Conclusions: AIs caused bone loss, leading to ORFR as measured by BMD, FRAX, and TBS. Because FRAX and TBS are derived from DXA and patient history, a combined screening approach may efficiently and cost-effectively identify additional BC patients with ORFR who may benefit from ART. Screening Method Pts with ORFR (%) Before AI After AI BMD alone 3/74 (5%) 5/74 (7%) BMD+FRAX 7/74 (9%) 11/74 (15%) BMD+TBS 12/74 (16%) 16/74 (22%) BMD+FRAX+TBS 15/74 (20%) 20/74 (27%)
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".