Development of target‐specific treatments in multiple myeloma
Bibliographic record
Abstract
In the last decade, the novel agents lenalidomide, bortezomib, and thalidomide have dramatically improved outcomes for patients with multiple myeloma (MM). A number of new therapies with precise targets involved in MM cell growth and replication are now in development and have the potential for further improvements. Second-generation proteasome inhibitors and thalidomide derivatives may offer increased efficacy and safety. Investigational therapies with rationally selected targets in MM include inhibitors of histone deacetylase, heat shock protein 90, mammalian target of rapamycin, BCL2, Akt, mitogen-activated protein kinase, and telomerase. In addition, monoclonal antibodies directed against several targets have been developed and many are showing promise in initial clinical trials in MM. Interest in the ancient remedy of arsenic trioxide has been revived because of its proapoptotic effects on mitochondria, despite its established toxicities. In general, combination regimens are proving the most efficacious, which is to be expected given the multiple overlapping pathways responsible for MM growth and progression.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".