Impact of COVID-19 on outcomes with teclistamab in patients with relapsed/refractory multiple myeloma in the phase 1/2 MajesTEC-1 study
Bibliographic record
Abstract
The risk of infection in patients with multiple myeloma (MM) is high, particularly for those with relapsed/refractory MM (RRMM), who typically exhibit substantial immune dysfunction due to multiple prior therapies as well as MM itself [ 1 , 2 ]. The recent COVID-19 pandemic had an impact on patients with MM compared with non-MM patients, including a higher risk of infection, a higher excess mortality rate, and decreased survival in 2020 compared with 2019 [ 3 ]. Patients with MM are now known to be particularly vulnerable to COVID-19 infection [ 4 ]; COVID-19 mortality rates of up to 57% have been reported in patients with MM across different institutions [ 5 ], with hematologic cancers associated with higher infection severity and mortality than other tumor types [ 6 ].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".