Many People with Chronic Lymphocytic Leukemia or Small Lymphocytic Lymphoma Benefit From Ibrutinib Treatment up to 8 Years: A Plain Language Summary
Bibliographic record
Abstract
Plain Language SummaryThis is a plain language summary of a publication describing long-term results from the RESONATE-2 study with up to 8 years of follow-up. The original paper was published in Blood Advances in June 2022.What were the results? Researchers looked at 269 adults with chronic lymphocytic leukemia (CLL) or small lymphocytic lymphoma (SLL) who had not received any treatment for their CLL/SLL. Study participants were randomly divided into two groups: 136 participants received treatment with a drug called ibrutinib, and 133 participants received treatment with a drug called chlorambucil. Participants in the study were treated and followed for up to 8 years, with results showing that more participants who took ibrutinib (59%) were alive without worsening of their disease at 7 years after starting treatment than participants who took chlorambucil (9%). Almost half of the participants (42%) were able to stay on ibrutinib treatment for up to 8 years.What do the results of the study mean? In people with CLL or SLL, more participants who were taking ibrutinib were alive without worsening of their disease after 7 years compared with participants who took chlorambucil. Clinical Trial Registration: NCT01722487 (ClinicalTrials.gov) Clinical Trial Registration: NCT01724346 (ClinicalTrials.gov)To read the full Plain Language Summary of this article, click here to view the PDF.Link to original article here
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".