Incidence of and risk factors for major haemorrhage in patients treated with ibrutinib: An integrated analysis
Bibliographic record
Abstract
Ibrutinib, a Bruton tyrosine kinase inhibitor, is approved for treatment of various B-cell malignancies. In ibrutinib clinical studies, low-grade haemorrhage was common, whereas major haemorrhage (MH) was infrequent. We analysed the incidence of and risk factors for MH from 15 ibrutinib clinical studies (N = 1768), including 4 randomised controlled trials (RCTs). Rates of any-grade bleeding were similar for single-agent ibrutinib and ibrutinib combinations (39% and 40%). Low-grade bleeding was more common in ibrutinib-treated than comparator-treated patients (35% and 15%), and early low-grade bleeding was not associated with MH. The proportion of MH in RCTs was higher with ibrutinib than comparators (4.4% vs. 2.8%), but after adjusting for longer exposure with ibrutinib (median 13 months vs. 6 months), the incidence of MH was similar (3.2 vs. 3.1 per 1000 person-months). MH led to treatment discontinuation in 1% of all ibrutinib-treated patients. Use of anticoagulants and/or antiplatelets (AC/AP) during the study was common (~50% of patients) and had an increased exposure-adjusted relative risk for MH in both the total ibrutinib-treated population (1.9; 95% confidence interval, 1.2-3.0) and RCT comparator-treated patients (2.4; 95% confidence interval, 1.0-5.6), indicating that ibrutinib may not alter the effect of AC/AP on the risk of MH in B-cell malignancies.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".