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Record W4306871090 · doi:10.1093/eurheartj/ehac464

Factor XI inhibitors: cardiovascular perspectives

2022· review· en· W4306871090 on OpenAlexaff
Raffaele De Caterina, Domenico Prisco, John W. Eikelboom

Bibliographic record

VenueEuropean Heart Journal · 2022
Typereview
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineIntensive care medicineClinical trialThrombosisRisk factorAtrial fibrillationDiseaseDiscovery and development of direct thrombin inhibitorsInternal medicineThrombinPlatelet

Abstract

fetched live from OpenAlex

Anticoagulants are the cornerstone for prevention and treatment of thrombosis but are not completely effective, and concerns about the risk of bleeding continue to limit their uptake. Animal studies and experience from patients with genetic coagulation factor XI deficiency suggesting that this factor is more important for thrombosis than for haemostasis raises the potential for drugs that target factor XI to provide safer anticoagulation. Multiple factor XI inhibitors are currently under evaluation in clinical trials, including parenterally administered antisense oligonucleotides, monoclonal antibodies, and orally active small-molecule inhibitors. Promising results of phase 2 trials in patients undergoing major orthopaedic surgery, and in those with end-stage kidney disease, atrial fibrillation and acute coronary syndromes have led to large phase 3 trials that are currently ongoing. We here review premises for the use of these agents, results so far accrued, ongoing studies, and perspectives for future patient care.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.125
GPT teacher head0.349
Teacher spread0.224 · 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

Citations67
Published2022
Admission routes1
Has abstractyes

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