Romosozumab: a novel bone anabolic treatment option for osteoporosis?
Bibliographic record
Abstract
Research into the drug romosozumab began with the investigation of patients with excess bone formation. The understanding of the wingless-type mouse mammary tumor virus integration site (Wnt) signaling pathway in bone metabolism identified the negative regulator of bone mass sclerostin as a potential target for the treatment of osteoporosis. Preclinical studies confirmed this idea because they showed that sclerostin antibodies have the potential to increase bone formation. Biochemical analyses of clinical studies showed a significant increase in bone formation markers, which then slowly decreased within a year. This was accompanied by a particularly initially pronounced decrease in bone resorption. This dual mechanism of action led to an increase in bone mineral density and a significant reduction in fracture risk. Clinical vertebral fractures decreased by between 28 and 36%, nonvertebral fractures shown in a post hoc analysis by 42%. Romosozumab is administered once a month in the form of two injections. At the puncture site, reactions occur in about 5%. The most significant side effects are cardiovascular. In phase III studies, the number of serious cardiovascular complications was not significantly, albeit numerically, higher than in the control group. In Japan, South Korea, Canada, Australia, and the USA, osteoporosis patients at a high risk of fracture may already be treated with romosozumab (Evenity). Approval in the European Union was granted by 2019-12-12.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".