How We Manage Patients with Indolent B-Cell Malignancies on Bruton’s Tyrosine Kinase Inhibitors: Practical Considerations for Nurses and Pharmacists
Bibliographic record
Abstract
The most common forms of B-cell malignancy, non-Hodgkin lymphoma (NHL) and chronic lymphocytic leukemia (CLL), have seen a drastic shift in the treatment landscape over the last two decades with the introduction of targeted agents. Among them are Bruton's tyrosine kinase (BTK) inhibitors, which have demonstrated excellent efficacy in indolent B-cell NHLs and CLL. Although BTK inhibitors are generally thought to be more tolerable than chemoimmunotherapy, they are associated with a unique safety profile including varying rates of rash, diarrhea, musculoskeletal events, cardiovascular events, and bleeding. Ibrutinib was the first BTK inhibitor to gain a Health Canada indication, followed by second-generation BTK inhibitors acalabrutinib and zanubrutinib, which have better safety profiles compared to ibrutinib, likely due to their improved selectivity for BTK. As BTK inhibitors are oral agents given continuously until disease progression, long-term adverse event (AE) monitoring and management as well as polypharmacy considerations are important for maintaining patient quality of life. This paper intends to serve as a reference for Canadian nurses and pharmacists on dosing, co-administration, and AE management strategies when caring for patients with indolent B-cell NHL or CLL being treated with BTK inhibitors.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".