First Report of Treatment of Chronic Lymphocytic Leukemia in a Patient With Cystic Fibrosis
Bibliographic record
Abstract
Patients with cystic fibrosis (CF) are known to have an increased risk of developing hematological malignancies. Despite this, there is a paucity of evidence to guide treatment decisions in this population, which requires special consideration due to patients’ risk of developing infection and baseline relative immunosuppression. There are 11 prior published reports of treatment of hematological malignancy in CF, and no reports of treatment of chronic lymphocytic leukemia (CLL). We report the first case of treatment of CLL in a 51-year-old male with CF. He was successfully treated with bendamustine concomitantly with prophylactic antibiotics, antifungals, and intravenous immunoglobulin (IVIG). He obtained a complete remission (CR) without any infectious complications. Following this, he relapsed and was treated to near CR with ibrutinib and monthly IVIG, with manageable infectious complications. We have shared this case with a view to contribute to the literature in this area. In particular, we have demonstrated that achieving CR in CLL without infectious complications is possible using bendamustine monotherapy, prophylactic antibiotics, and IVIG. Second, we have showed that achieving significant response with manageable infectious complications is possible with ibrutinib monotherapy and monthly IVIG. J Med Cases. 2017;8(1):20-23 doi: https://doi.org/10.14740/jmc2708w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".