Intravenous low-dose anti-CD20 Rituximab infusion contrasted with subsequent subcutaneous Rituximab injection during CLL patient treatment 3374
Bibliographic record
Abstract
Abstract Description We evaluated high frequency (2/week) low dose (50 mg) (HFLD) anti-CD20 monoclonal antibody (mAb), rituximab (RTX), during treatment of progressive CLL patients (PMID 36689726). The initial RTX dose was by intravenous (IV) infusion to monitor for potential reactions. Subsequent RTX doses were by subcutaneous (SQ) injection to reduce clinical monitoring and allow home administration. Blood specimens were collected during the first week of therapy: before IV infusion, 1h during, after infusion completion, 48h later (before and after first SQ injection), and 7 days later (before next RTX injection). The initial IV RTX decreased the median circulating CLL cell count by 84% from baseline. Maximal depletion of CLL cells was rapid and complete within 1h of starting RTX infusion. In contrast, CLL CD20 levels continue to decline at the end of infusion. Mobilization of CLL cells was evidenced by a partial rebound in CLL cell counts and CD20 levels after 48h and 7 days. SQ RTX injection adds complexity to data interpretation because of delayed RTX entry into circulation, resulting in delayed RTX serum level kinetics compared to IV infusion. Our data suggest slow SQ mAb diffusion into circulation steadily binds to CLL cells, resulting in low serum levels and slow depletion of CLL cells, with increasing serum RTX levels as SQ doses accumulate. Surviving CLL cells were still sensitive to mAb cytotoxicity, suggesting that cytotoxic effector cells were “exhausted” by RTX therapy. Funding Sources We are grateful for research funding from the NCI (R21CA267040), Acerta/AstraZeneca, the Cadregari Foundation at the University of Rochester, generous gifts from Mr. Lawrence Halpern, and generous gifts from Ms. Elizabeth Aaron. Topic Categories Translational and Interventional Immunology (TI)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".