Factor XI inhibitors: what should clinicians know
Bibliographic record
Abstract
PURPOSE OF REVIEW: Factor XI (FXI) inhibitors were developed to address unmet needs and limitations of current anticoagulants and are currently being studied in several indications. In this paper, we review the rationale for the development of these agents and summarize what clinicians should know about drugs that target FXI. RECENT FINDINGS: Patients with FXI deficiency may have a lower risk of venous thromboembolism and cardiovascular events and have a variable but generally mild bleeding diathesis. FXI has been proposed as a target for anticoagulants due to the potential for reduction in thrombosis with a lower risk of bleeding than current anticoagulant agents. Several classes of drugs that target FXI are under development, of which three classes (small molecule inhibitors, antisense oligonucleotides and monoclonal antibodies) have been studied in Phase II trials. At least three large Phase III trial programs are planned or are underway, and will study the efficacy and safety of FXI inhibitors in tens of thousands of patients across a variety of indications including atrial fibrillation, stroke and cancer-associated venous thromboembolism. SUMMARY: FXI inhibitors were developed with the hope of attenuating thrombosis with reduced bleeding/impairment of haemostasis. These agents have shown promise in preliminary trials with a low rate of bleeding. Ongoing Phase III investigations will inform the utility of these agents in clinical practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".