Impact of Split Dosing the First Rituximab Infusion in Patients with High Lymphocyte Count
Bibliographic record
Abstract
The most common adverse reactions to rituximab are infusion-related reactions (IRR). We evaluated the efficacy of split dosing the first rituximab infusion over two days to reduce IRR incidence in patients with hematological cancer and a high lymphocyte count. This is a retrospective observational study conducted in two healthcare centers in Quebec, Canada. The study enrolled patients with white blood cell counts ≥25.0 × 109/L who received their first rituximab dose for hematological cancer between December 2007 and May 2020. One healthcare center used asymmetrical split dosing, while the other used symmetrical split dosing. A total of 183 treatment episodes were collected from 143 patients. Among patients who received a fractionated dosing schedule, 42% developed an IRR from the first rituximab infusion compared with 50% for the standard protocol (adjusted relative risk, 0.89; p = 0.540). No significant difference was observed in IRR severity between either groups. However, 24% of patients who received the asymmetrical protocol developed an IRR compared to 68% for the symmetrical protocol (adjusted relative risk, 0.32; p = 0.003). These results suggest that an asymmetrical split dosing could be effective in reducing the incidence of IRR and is preferable to a symmetrical one.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".