Underrepresentation of Small Lymphocytic Lymphoma in Clinical Trials for Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
BACKGROUND: Although chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are the same biologic disease entity and warrant identical treatment approaches, patients with SLL have frequently been excluded from clinical trials in CLL. METHODS: This study assessed the representation of patients with SLL among Phase II or III clinical trials cited in the 2024 National Comprehensive Cancer Network (NCCN) treatment guidelines. RESULTS: Patients with SLL were explicitly eligible for only 21 (38%) of the 56 clinical trials for CLL, comprising 222 (6%) of the 3440 enrolled patients. Notably, 380 patients with SLL were enrolled in 16 separate non-CLL clinical trials alongside patients with indolent B-cell lymphomas such as follicular lymphoma. In CLL trials, patients with SLL were included in a greater proportion of studies evaluating BTK inhibitors (67%) or BTK/BCL2 inhibitor combinations (67%) compared to BCL2 inhibitors (0%) or chemoimmunotherapy (0%). CONCLUSIONS: Although recent and upcoming trials show a promising trend toward the inclusion of patients with SLL, further advocacy is needed to raise awareness of the biological similarities between CLL and SLL and to promote the representation of patients with SLL in CLL/SLL clinical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.477 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".