Utility of trabecular bone score in the evaluation of osteoporosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Trabecular bone score (TBS) is a lumbar spine dual-energy absorptiometry texture index which provides information on skeletal quality partially independent of bone mineral density (BMD). A body of work has emerged demonstrating the relationship between TBS and fracture risk, with lower TBS values associated with increased risk for osteoporotic fracture in postmenopausal women and older men. TBS is derived from standard DXA images; however, the information provided by TBS is complementary to that provided by BMD. In this article, we review the current state of TBS and its evolving role in the assessment and management of osteoporosis, with particular emphasis on the literature of the previous year. RECENT FINDINGS: TBS-adjusted The Fracture Risk Assessment tool (FRAX) probabilities enhance fracture risk prediction compared with conventional FRAX predictions. TBS has been found to better categorize fracture risk and assists in FRAX-based treatment decisions, particularly for patients close to an intervention threshold. However, change in lumbar spine TBS while undergoing antiresorptive treatment is not a useful indicator of antifracture effect. SUMMARY: Lumbar spine TBS is a recently developed image-based software technique for skeletal assessment, complementary to conventional BMD, which has been shown to be clinically useful as a fracture risk prediction tool.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".