Clinical decisions and factors influencing the management of persons with hemophilia requiring antithrombotic therapy: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Persons with hemophilia face challenges when requiring antithrombotic therapy due to competing bleeding and thrombosis risks. The absence of robust evidence complicates clinical decision-making, relying on expert opinions and consensus. RESEARCH DESIGN AND METHODS: To explore the decision-making processes of physicians managing antithrombotic therapy in persons with hemophilia, identify key factors shaping clinical judgment, and develop a decision-making framework to improve patient care and research. We conducted a qualitative study grounded in constructivist methodology, recruiting seven Canadian physicians with expertise in hemophilia and/or thromboembolic disorders. Three virtual focus groups were held and analyzed using reflexive thematic analysis. Themes were developed iteratively to identify key components. RESULTS: Participants described five themes involving initial and continuous risk assessment of bleeding and thrombosis, selection of safe antithrombotic therapies or alternatives, and development of hemophilia-specific treatment plans. They highlighted the need for periodic reassessment of strategies and emphasized individualized, co-produced care. Each framework element encompassed multiple factors influencing decision-making toward patient-centered care. CONCLUSIONS: This study provides a decision-making framework to guide antithrombotic therapy in persons with hemophilia. By integrating risk assessments, individualized care, and shared decision-making, the framework addresses this high-risk context. Future research should validate the framework and incorporate patient perspectives to enhance 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".