Incidence of late onset neutropenia associated with rituximab use in B cell lymphoma patients undergoing autologous stem cell transplantation
Bibliographic record
Abstract
Reversible late onset neutropenia associated with rituximab has been reported with incidence rates varying from 15 to 70% in B cell lymphoma patients receiving autologous stem cell transplantation. We conducted a retrospective descriptive study at one tertiary care center in adult B cell lymphoma patients treated with rituximab and autologous stem cell transplantation between 1 January 2004 and 30 June 2014. Late onset neutropenia was defined as an absolute neutrophil count <1.0 × 10 9 cells/L after neutrophil engraftment and less than six months post autologous stem cell transplantation. The primary objective was to determine the incidence of late onset neutropenia. The secondary objectives were to examine whether the use of rituximab with re-induction therapy, mobilization or high dose chemotherapy regimens increased the risk for late onset neutropenia, and to evaluate infectious complications. Of 315 subjects, 92 (29.2%) developed late onset neutropenia. Mobilization regimens containing rituximab (OR 2.90 95% CI: 1.31–6.40), high dose chemotherapy containing rituximab (OR 1.87 95% CI: 1.14–3.05), and exposure to rituximab in either or both regimens (OR 3.05 95% CI: 1.36–6.88) significantly increased the risk of late onset neutropenia. While neutropenic, 17.4% experienced an infection, 7.6% experienced febrile neutropenia, and 5.4% were hospitalized. In conclusion, rituximab with mobilization or high dose chemotherapy may increase the risk of late onset neutropenia post autologous stem cell transplantation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 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".