Bibliographic record
Abstract
FigureNEW YORK—Treatment regimens for relapsed/refractory multiple myeloma have become plentiful in the past few years, with a panoply of options. Consequently, “the old rules no longer apply,” says James Berenson, MD, President and Chief Medical Officer of the Institute for Myeloma and Bone Cancer Research in West Hollywood, Calif., speaking here at the International Congress on Hematologic Malignancies. Before anything, he said, it is paramount to confirm disease progression—“so don't rely on just one lab value, which might be subjective.” His recommendations for new or at least newer rules: Dose escalation can be helpful. Patients who have disease progression from a drug at one dose may respond to the same drug at a higher dose, as with lenalidomide or thalidomide. “Antibiotics may work, believe it or not,” he said, specifically mentioning clarithromycin. Try to use drugs the patient has not received before. However, progression on one drug in combination does not mean that drug will not be effective with another agent. For example, patients with disease progression from bortezomib with melphalan often respond to bortezomib with pegylated liposomal doxorubicin, he said. Even different drugs in the same class may be active—for example, patients who do not respond to bortezomib-melphalan may respond to other alkylating agents such as bendamustine or cyclophosphamide; disease that fails to respond to lenalidomide may respond to thalidomide and vice versa; and bortezomib combinations that have been ineffective may produce responses with replacement with carfilzomib. Although regimens may be ineffective alone, when combined they may be effective: Patients who do not respond to lenalidomide-dexamethasone or to bortezomib-pegylated liposomal doxorubicin often respond to the combination of both regimens. The same combination may be effective again if the patient has not received the combination in a long time. Be careful interpreting renal insufficiency—often it may actually be due to anemia or diabetes rather than to myeloma, he said. European/Canadian study data may not be relevant to U.S. patients, who have many more treatment options. FigureJAMES BERENSON, MD. JAMES BERENSON, MDAfter his talk, he was asked from the audience about transplants. “The era of transplants is over,” he replied. “I do not do transplants anymore. “Myeloma is a marathon, not a sprint. It's better to run a 10-minute mile and finish the race than run a four-minute mile and drop out at mile 5,” said Berenson, a marathon runner himself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.386 | 0.239 |
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".