Bone microarchitecture assessed by TBS predicts osteoporotic fractures independent of bone density: The manitoba study
Bibliographic record
Abstract
The measurement of BMD by dual-energy X-ray absorptiometry (DXA) is the "gold standard" for diagnosing osteoporosis but does not directly reflect deterioration in bone microarchitecture. The trabecular bone score (TBS), a novel gray-level texture measurement that can be extracted from DXA images, correlates with 3D parameters of bone microarchitecture. Our aim was to evaluate the ability of lumbar spine TBS to predict future clinical osteoporotic fractures. A total of 29,407 women 50 years of age or older at the time of baseline hip and spine DXA were identified from a database containing all clinical results for the Province of Manitoba, Canada. Health service records were assessed for the incidence of nontraumatic osteoporotic fracture codes subsequent to BMD testing (mean follow-up 4.7 years). Lumbar spine TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Osteoporotic fractures were identified in 1668 (5.7%) women, including 439 (1.5%) spine and 293 (1.0%) hip fractures. Significantly lower spine TBS and BMD were identified in women with major osteoporotic, spine, and hip fractures (all p < 0.0001). Spine TBS and BMD predicted fractures equally well, and the combination was superior to either measurement alone (p < 0.001). Spine TBS predicts osteoporotic fractures and provides information that is independent of spine and hip BMD. Combining the TBS trabecular texture index with BMD incrementally improves fracture prediction in postmenopausal women.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".