Efficacy of Low Dose Denosumab in Maintaining Bone Mineral Density in Postmenopausal Women With Osteoporosis Switching From 60mg to 30mg 6 Monthly: A Real World, Prospective Observational Study
Bibliographic record
Abstract
Abstract Introduction: Denosumab, a fully human monoclonal antibody to RANK-ligand, has been shown to increase bone mineral density (BMD) and reduce the risk of fracture in postmenopausal women with osteoporosis. Cessation of denosumab is associated with rises in bone remodelling, reductions in BMD and an increased risk of fracture. The primary objective of this study is to evaluate the efficacy of low dose denosumab (30mg/6 months) in preventing bone loss in postmenopausal women with osteoporosis switching from 60mg to 30mg every 6 months. We report the effects of low dose denosumab for upto 2years in patients previously treated with denosumab for >=3 years as well as < 3years. Methods: Following informed consent, postmenopausal women with osteoporosis who had been on denosumab 60mg every 6 months were switched to receive 30mg of denosumab every 6 months.. Patients with an additional skeletal disorder, prior fragility fracture, or on oral steroids (daily in the past 12 months) were excluded. The primary endpoint was the percent change in BMD at the lumbar spine (LS), total hip (HP), femoral neck (FN) and 1/3 radius (1/3R) at 12 and 24 months. Secondary outcomes were adverse effects and fracture Results: 127 patients were included in the study. 44 patients had received 60 mg for 3 years or longer before transitioning to 30mg and 83 patients switched before completing 3 years on full dose therapy. Patients on less than 3yrs of 60mg therapy before the switch showed a significant improvement in BMD at LS (+2.00%, 95% CI 0.49% to 3.51%, n = 55, p-value = 0.01) 1 year post transition. There were no significant changes at the FN, TH or 1/3 radial sites 1 year post transition compared to baseline. At 2 years post transition (n=35) significant changes were noticed at LS (+4.65%, 95% CI 2.29% to 7.01%, p value <0.001), FN (+ 4.87%, 95% CI 1.46% to 8.28%, p value = 0.006) and 1/3 radial sites (+4.95%, 95% CI 0.73% to 9.17%, p value = 0.02). No significant changes were noted at TH. Similar results were seen with prior denosumab therapy for <3years No fractures were observed in this observational study. Conclusions: Switching from 60mg of denosumab to 30 mg every 6 months was not associated with reductions in BMD and may be a valuable treatment option in patients who have completed long term denosumab therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".