A multicenter review of infusion-related reactions to daratumumab for relapsed multiple myeloma in the real world setting
Bibliographic record
Abstract
BACKGROUND: Daratumumab is used in the treatment of relapsed multiple myeloma. Daratumumab infusion-related reactions can occur with the highest incidence on the first infusion. METHODS: A retrospective review of all daratumumab infusions used as part of the DVd and DRd regimens for relapsed multiple myeloma was undertaken. The review of infusion-related reactions was conducted by reviewing the treatment room nursing note on the days that daratumumab was administered. If the patient experienced an infusion-related reaction, then the data captured included if the full dose was administered. RESULTS: Daratumumab infusion-related reactions occurred most frequently on the first dose. The rates of infusion-related reactions using a split dose approach for daratumumab administration were lower than that reported in clinical trials. All of the infusion-related reactions were managed with appropriate interventions in the outpatient setting. The adoption of rapid infusion daratumumab beginning with cycle 2 of DVd and DRd was well tolerated. CONCLUSIONS: Our experience of daratumumab infusions using a split dose approach was associated with an infusion-related reaction rate in 28% of patients on cycle 1, day 1 of DVd and DRd regimens. All patients were able to complete full doses of daratumumab by utilizing split dose. The rates of daratumumab infusion-related reactions are highest on the first infusion. In addition, our adoption of rapid infusion daratumumab was safe.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".