Challenging the Current Approaches to Multiple Myeloma-Related Bone Disease: From Bisphosphonates to Target Therapy
Bibliographic record
Abstract
Bone disease (BD) is the hall-mark clinical feature of multiple myeloma (MM), accounting up to 60% of patients with bone pain at diagnosis and 60% with a pathologic fracture during the course of their disease. Experimental models, which recapitulate in vivo the human bone marrow microenvironment (HBMM) in immunodeficient mice have been recently developed as valuable tool for the study of MM pathophysiology as well as the experimental treatment of BD. At present, bisphosphonates are the mainstay treatment of MM-related BD. The growing information on the cellular and molecular bases of BD as well as the availability of novel anti-resorptive agents, such as the IgG1-anti-RANKL (AMG 161) Denosumab, are now depicting a new scenario where the treatment will be afforded by the use of different agents. Furthermore the availability of highthroughput molecular profiling approaches, including DNA microarrays and proteomics, is likely to provide new platforms for patients stratification and treatment individualization on specific targets. It is now the right time for a therapeutical approach which is rationally based on the complexity of the biopathology of MM-related BD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".