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Current understanding of bleeding with ibrutinib use: a systematic review and meta-analysis

2017· review· en· W2613723809 on OpenAlexafffund
François Caron, Darryl P. Leong, Christopher Hillis, Graeme Fraser, Deborah Siegal

Bibliographic record

VenueBlood Advances · 2017
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersMcMaster UniversityCanadian Institutes of Health ResearchHamilton Health Sciences
KeywordsIbrutinibMeta-analysisMedicineConfidence intervalInternal medicineRelative riskClinical trialLeukemia

Abstract

fetched live from OpenAlex

Ibrutinib therapy was associated with an increased risk of bleeding in previous trials. In this systematic review and meta-analysis of published trials including patients treated with ibrutinib, the relative risk (95% confidence interval [CI]) of overall bleeding was significantly higher in ibrutinib recipients (2.72 [1.62-6.58]), but major bleeding did not show a significant difference (1.66 [0.96-2.85]). The incidences (95% CI) of major bleeding and any bleeding were 3.0 (2.3-3.7) and 20.8 (19.1-22.1) per 100 patient-years, respectively. This analysis is limited by reporting bias from variable ascertainment of bleeding and lack of allocation concealment in some studies and differing exposures between groups, leading to potential overestimation of event rates in the ibrutinib group.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.022
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.389
GPT teacher head0.468
Teacher spread0.079 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations97
Published2017
Admission routes2
Has abstractyes

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