Monthly treatment with romosozumab for 1 year increases bone mineral at the hip, but not the knee, in women with chronic spinal cord injury
Bibliographic record
Abstract
Abstract Bone loss below the level of neurological lesion is a well-known complication of spinal cord injury (SCI). To date, most research has focused on pharmaceutical intervention using antiresorptives to prevent bone loss during the acute phase of SCI; however, limited research has investigated treatments for established osteoporosis during chronic SCI. Romosozumab, a monoclonal antibody with both antiresorptive and anabolic effects, has demonstrated significant increases in BMD for women with established PMO. Therefore, the purpose of this study was to examine the efficacy of monthly treatment with romosozumab to improve DXA-derived areal BMD at the hip, and CT-derived BMC and strength at the hip and knee in women with chronic SCI and an inability to ambulate. Twelve female participants with chronic SCI were recruited to receive 1 yr of monthly subcutaneous injections of romosozumab (210 mg). DXA and CT scans were taken at baseline, and months 3, 6, and 12 to quantify bone mineral, and finite element (FE) analysis was used to predict bone strength. Longitudinal mixed effects models were employed to determine the impact of treatment on bone properties. After 12 mo of treatment, areal BMD at the lumbar spine and total hip were significantly increased with median changes of 10.2% (IQR: 8.3–15.2%, p<.001) and 4.2% (IQR: 3.4–7.7%, p = .009), respectively. Improvements at the hip were primarily due to increases in trabecular, not cortical, bone and effects were sufficient to significantly increase FE-predicted strength by 20.3% (IQR: 9.5–37.0%, p = .004). Treatment with romosozumab did not lead to any significant improvement in bone mineral at the distal femur or proximal tibia. These findings provide promising results for romosozumab treatment to improve bone mineral and reduce fracture risk at the hip, but not the knee, in women with chronic SCI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".