Denosumab treatment of giant cell tumors in the spine induces woven bone formation
Bibliographic record
Abstract
Giant cell tumors of bone (GCTB) are rare but aggressive, locally destructive tumors. They typically affect young people, significantly reducing their quality of life and increasing mortality rates. Giant cell tumors of bone are composed of osteoclast-like giant cells that respond to increased secretion of RANKL by stromal cells, triggering osteolysis. For over a decade, denosumab, a monoclonal antibody targeting this receptor activator, has been approved as a neo-adjuvant to facilitate surgical resection or in the setting of inoperable tumors. Denosumab treatment has shown rapid pain improvement and tumor size reduction in the spine. Although variable degrees of tumor mineralization have been observed in clinical applications of this drug, the nature of this newly formed mineralized tissue has yet to be determined. To characterize both mineralization and collagen organization in the newly formed bone, we conducted extensive analyses on 4 posttreatment giant cell tumor vertebral samples, involving quantitative backscattered imaging, electron probe microanalysis, and a novel method for determining the alignment of collagen fibrils using second harmonic generation. Additionally, biological mechanisms involved in bone mineralization and matrix formation were analyzed using histological staining and mass spectroscopy. Our results concluded that denosumab treatment after giant cell tumor of bone in the spine was associated with the formation of woven bone and increased mineral density in a matrix of disorganized collagen fibers characterized by increased collagen III content, with the response appearing to depend on patient age and extension of treatment. To our knowledge, this is the first comprehensive material-based study on the bone formed during denosumab treatment for GCTB, providing valuable information on how denosumab affects bone quality and how the reported methodology can be applied to similar studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".