Current Gaps in the Provision of Safe and Effective Anticoagulation in Atrial Fibrillation and the Potential for Factor XI-Directed Therapeutics
Bibliographic record
Abstract
The global prevalence of atrial fibrillation is rapidly increasing, in large part due to the aging of the population. Atrial fibrillation is known to increase the risk of thromboembolic stroke by 5 times, but it has been evident for decades that well-managed anticoagulation therapy can greatly attenuate this risk. Despite advances in pharmacology (such as the shift from vitamin K antagonists to direct oral anticoagulants) that have increased the safety and convenience of chronic oral anticoagulation in atrial fibrillation, a preponderance of recent observational data indicates that protection from stroke is poorly achieved on a population basis. This outcomes deficit is multifactorial in origin, stemming from a combination of underprescribing of anticoagulants (often as a result of bleeding concerns by prescribers), limitations of the drugs themselves (drug-drug interactions, bioaccumulation in renal insufficiency, short half-lives that result in lapses in therapeutic effect, etc), and suboptimal patient adherence that results from lack of understanding/education, polypharmacy, fear of bleeding, forgetfulness, and socioeconomic barriers, among other obstacles. Often this adherence is not reported to treating clinicians, further subverting efforts to optimize care. A multidisciplinary, interprofessional panel of clinicians met during the 2023 International Society of Thrombosis and Haemostasis Congress to discuss these gaps in therapy, how they can be more readily recognized, and the potential for factor XI-directed anticoagulants to improve the safety and efficacy of stroke prevention. A full appreciation of this potential requires a reevaluation of traditional teaching about the "coagulation cascade" and decoupling the processes that result in (physiologic) hemostasis and (pathologic) thrombosis. The panel discussion is summarized and presented here.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".