Bibliographic record
Abstract
For a long time, malignant blood cancers faced great difficulty in development of successful treatments due to their mobility and evasive nature. Of these conditions, multiple myeloma (MM) is an untreatable cancer due to its highly relapsing and refractory nature, which will eventually dissipate all efforts in controlling the disease. Previous treatments only control the progression of myeloma to an extent and prolong patients’ lives shortly. Thus, multiple myeloma patients are in dire need of new treatment options to prevent or postpone the eventual relapse. The discovery and development of CAR-T therapy show promising results for MM treatment. Recently approved therapies by the FDA, Abecma and Carvykti, displayed high response rates with low relapses in patients who underwent the drug trials. However, therapeutic applications of CAR-T have encountered various obstacles. The treatment is largely associated with cytokine release syndrome and other adverse events, ranging from systematic to organ toxicities. In addition, specificity and cost are pressing issues that seek solutions. Despite difficulties, many CAR-T options targeting MM are under active research and investigation. With further development and optimization in additional drug trials, the application of CAR-T therapy can offer a new approach to controlling multiple myeloma for those suffering from drug resistance.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".