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Record W3201325178 · doi:10.3390/cancers13184606

Thrombotic Complications Associated with Immune Checkpoint Inhibitors

2021· review· en· W3201325178 on OpenAlexaff
Tzu‐Fei Wang, Alok A. Khorana, Marc Carrier

Bibliographic record

VenueCancers · 2021
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineThrombosisAdverse effectVenous thrombosisComplicationCancerPopulationImmune systemIncidence (geometry)Intensive care medicineInternal medicineOncologyImmunology

Abstract

fetched live from OpenAlex

Thromboembolism is a common complication in patients with cancer and is associated with significant morbidity and mortality. Anticancer treatment is a known risk factor of cancer-associated thrombosis. Immune checkpoint inhibitors have become a mainstay of treatment in various cancers. Both venous and arterial thrombosis have been increasingly reported as adverse events associated with immune checkpoint inhibitors in recent studies, with a cumulative incidence of venous thrombosis to be 5-8% at 6 months and over 10% at 12 months. Additionally, rates of approximately 1-5% for arterial thrombosis were reported at 12 months. Data also showed an association of thromboembolism with adverse survival. Many pertinent clinical questions in this population deserve further investigation, including the risks of thrombosis associated with immune checkpoint inhibitors as compared to those with traditional systemic therapy, associated risk factors, and the optimal prevention and treatment strategies. In this review, we synthesize data from available literature, provide relevant information for clinicians and potential future directions for 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.334
Teacher spread0.282 · 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 teacher head, not a consensus.

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

Citations44
Published2021
Admission routes1
Has abstractyes

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