Venous thromboembolism: Past, present and future
Bibliographic record
Abstract
Venous thromboembolism (VTE), the third most frequent acute cardiovascular syndrome, is associated with a considerable disease burden which continues to grow along with the longer life expectancy of the population worldwide. In the past century, parenteral heparin prophylaxis was established for hospitalised patients at elevated risk of VTE. More recently, non-vitamin K antagonist oral anticoagulants (NOACs) with a direct inhibiting effect on factor Xa or thrombin, underwent extensive testing in clinical trials and have been approved for patients undergoing hip or knee replacement. Clinical investigation is ongoing in further areas of thromboprophylaxis, including medical prophylaxis in patients and high-risk situations in the outpatient setting. The diagnostic approach to suspected VTE is now based on advanced imaging techniques and robust diagnostic algorithms which ensure high sensitivity and specificity. Nevertheless, the role of clinical, or pre-test, probability assessment remains crucial to avoid overdiagnosis and treatment errors. Advances in reperfusion strategies, along progressive establishment of the NOACs as the new standard of anticoagulation treatment, have simplified the management of VTE, improving outcomes and particularly safety. While new molecular targets for anticoagulation are being investigated in the quest to further reduce bleeding risk, adjusting the initial regimen to the patient's risk and finding the optimal duration of anticoagulation after an index VTE event will be some of the top priorities in the years to come. Importantly, and in parallel to new drugs and technical advances in imaging, incentives such as hospital accreditation and funding based on evidence-based practice need to be implemented to increase guideline adherence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".