Change in Trabecular Bone Score (TBS) With Antiresorptive Therapy Does Not Predict Fracture in Women: The Manitoba BMD Cohort
Bibliographic record
Abstract
Bone mineral density (BMD) and trabecular bone score (TBS), along with additional clinical risk factors, can be used to identify individuals at high fracture risk. Whether change in TBS in untreated or treated women independently affects fracture risk is unclear. Using the Manitoba (Canada) DXA Registry containing all BMD results for the population we identified 9044 women age ≥40 years with two consecutive DXA scans and who were not receiving osteoporosis treatment at baseline (baseline mean age 62 ± 10 years). We examined BMD and TBS change, osteoporosis treatment, and incident major osteoporotic fractures (MOFs) for each individual. Over a mean of 7.7 years follow-up, 770 women developed an incident MOF. During the interval between the two DXA scans (mean, 4.1 years), 5083 women initiated osteoporosis treatment (bisphosphonate use 80%) whereas 3961 women did not receive any osteoporosis treatment. Larger gains in both BMD and TBS were seen in women with greater adherence to osteoporosis medication (p for trend <0.001), and the magnitude of the increase was consistently greater for BMD than for TBS. Among treated women there was greater antifracture effect for each SD increase in total hip BMD change (fracture decrease 20%; 95% CI, 13% to 26%; p < 0.001), femoral neck BMD change (19%; 95% CI, 12% to 26%; p < 0.001), and lumbar spine BMD change (9%; 95% CI, 0% to 17%; p = 0.049). In contrast, change in TBS did not predict fractures in women who initiated osteoporosis treatment (p = 0.10). Among untreated women neither change in BMD or TBS predicted fractures. We conclude that, unlike antiresorptive treatment-related changes in BMD, change in lumbar spine TBS is not a useful indicator of fracture risk irrespective of osteoporosis treatment. © 2016 American Society for Bone and Mineral Research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".