Venous Thromboembolism and Its Treatment in High-Risk Groups
Bibliographic record
Abstract
Great advances have been made in the medical management of venous thromboembolism (VTE) and associated disorders. Nevertheless, prevention of deep-venous thrombosis (DVT) and the successful treatment of VTE in high-risk groups such as cancer patients remain significant challenges. At the 10th International Symposium on Thromboembolism, new results were presented and discussed regarding novel preventative measures and treatment strategies for VTE. This publication is based on the plenary sessions and the debate that took place during the symposium. The first series of articles covers topics relating to VTE and cancer patients, including mechanisms, risk factors, and prognosis for recurrent DVT; prevention of secondary DVT; and the use of low-molecular-weight heparins to prolong survival in patients with advanced cancer. The next two articles focus on VTE in nonsurgical patients. The first discusses the need for appropriate DVT prophylaxis in medical patients, which is lacking in comparison to the relatively high priority that DVT prophylaxis receives in surgical patients. The second article is a review of the epidemiology of VTE and strategies for the reduction of both primary and secondary VTE in the “real world,” outside the realms of cardiology and surgery. The thought provoking debate “Is Factor Xa a superior target to Factor IIa for antithrombotic therapies?” is reported in the final two contributions to this supplement to Seminars in Thrombosis and Hemostasis. To produce these proceedings, the Editors requested that the speakers write a review based on their presentation. This review was then checked for scientific accuracy and approved by the Editors. The Editors would like to express their sincere thanks to all who helped with the proceedings, especially the speakers/authors of the manuscripts and the attendees, without whom the symposium could not have happened. We would like to thank Remedica for their effective organization of the symposium and for their help in producing a permanent record of the meeting. Finally, thanks go to Pfizer for their generous sponsorship of both the meeting and this supplement.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".