Optimising the value of immunomodulatory drugs during induction and maintenance in transplant ineligible patients with newly diagnosed multiple myeloma: results from Myeloma XI, a multicentre, open‐label, randomised, Phase III trial
Bibliographic record
Abstract
Second-generation immunomodulatory agents, such as lenalidomide, have a more favourable side-effect profile than the first-generation thalidomide, but their optimum combination and duration for patients with newly diagnosed transplant-ineligible myeloma (ND-TNE-MM) has not been defined. The most appropriate delivery and dosing regimens of these therapies for patients at advanced age and frailty status is also unclear. The Myeloma XI study compared cyclophosphamide, thalidomide and dexamethasone (CTDa) to cyclophosphamide, lenalidomide and dexamethasone (CRDa) as induction therapy, followed by a maintenance randomisation between ongoing therapy with lenalidomide or observation for patients with ND-TNE-MM. CRDa deepened response but did not improve progression-free (PFS) or overall survival (OS) compared to CTDa. However, analysis by age group highlighted significant differences in tolerability in older, frailer patients that may have limited treatment delivery and impacted outcome. Deeper responses and PFS and OS benefits with CRDa over CTDs were seen in patients aged ≤70 years, with an increase in toxicity and discontinuation observed in older patients. Our results highlight the importance of considering age and frailty in the approach to therapy for patients with ND-TNE-MM, highlighting the need for prospective validation of frailty adapted therapy approaches, which may improve outcomes by tailoring treatment to the individual.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".