Relationship Between Bone Mineral Density <i>T</i>-Score and Nonvertebral Fracture Risk Over 10 Years of Denosumab Treatment
Bibliographic record
Abstract
Although treat-to-target strategies are being discussed in osteoporosis, there is little evidence of what the target should be to reduce fracture risk maximally. We investigated the relationship between total hip BMD T-score and the incidence of nonvertebral fracture in women who received up to 10 years of continued denosumab therapy in the FREEDOM (3 years) study and its long-term Extension (up to 7 years) study. We report the percentages of women who achieved a range of T-scores at the total hip or femoral neck over 10 years of denosumab treatment (1343 women completed 10 years of treatment). The incidence of nonvertebral fractures was lower with higher total hip T-score. This relationship plateaued at a T-score between -2.0 and -1.5 and was independent of age and prevalent vertebral fractures, similar to observations in treatment-naïve subjects. Reaching a specific T-score during denosumab treatment was dependent on the baseline T-score, with higher T-scores at baseline more likely to result in higher T-scores at each time point during the study. Our findings highlight the importance of follow-up BMD measurements in patients receiving denosumab therapy because BMD remains a robust indicator of fracture risk. These data support the notion of a specific T-score threshold as a practical target for therapy in osteoporosis. © 2019 The Authors Journal of Bone and Mineral Research published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".