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Record W2070487917 · doi:10.3109/10428194.2015.1022770

Management of adverse events associated with idelalisib treatment: expert panel opinion

2015· review· en· W2070487917 on OpenAlexaff
Steven Coutré, Jacqueline C. Barrientos, Jennifer R. Brown, Sven de Vos, Richard R. Furman, Michael J. Keating, Daniel Li, Susan O’Brien, John M. Pagel, Martin Poleski, Jeff P. Sharman, Nai-Shun Yao, Andrew D. Zelenetz

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2015
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsGilead Sciences (Canada)
FundersGilead Sciences
KeywordsIdelalisibMedicineAdverse effectInternal medicineDiarrheaIntensive care medicineIbrutinib

Abstract

fetched live from OpenAlex

Idelalisib is a first-in-class selective, oral, phosphatidylinositol 3-kinase delta (PI3Kδ) inhibitor approved for the treatment of several types of blood cancer. Idelalisib has demonstrated significant efficacy and a tolerable safety profile in clinical trials. However, the US prescribing information contains a black box warning for fatal and/or severe diarrhea or colitis, hepatotoxicity, pneumonitis and intestinal perforation. An expert panel was convened to review the pathology of these treatment-emergent adverse events (TEAEs) to propose key management tools for patients receiving idelalisib therapy. This article provides an overview of idelalisib TEAEs reported in clinical trials, and a summary of the panel's recommendations for identification and management of idelalisib treatment-emergent diarrhea or colitis as well as a discussion of transaminitis and pneumonitis. For idelalisib-related diarrhea or colitis (including unresolved grade 2 and grade ≥ 3), after exclusion of infectious causes, the panel recommends individualized treatment with budesonide or oral or intravenous steroid therapy.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.078
GPT teacher head0.351
Teacher spread0.273 · 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 designNot applicable
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

Citations307
Published2015
Admission routes1
Has abstractyes

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