New Horizons in Venous Thromboembolism Management: A Narrative Review
Bibliographic record
Abstract
Venous thromboembolism (VTE) remains a major cause of cardiovascular morbidity and mortality worldwide, and is a staple of daily clinical practice. While we have seen significant advancements in therapeutics over the last 20 years, several questions and controversies remain in the selection and duration of available therapies, as well as balancing the consequences of VTE and the bleeding risk imposed by treatment modalities. In recent years, new evidence based on randomized trials and registries have reshaped the therapeutic landscape. This narrative review synthesizes the latest advancements and future directions in VTE care, including recent guideline updates, new evidence pertaining to established pharmacologic therapy, risk stratification, interventional and procedural options, and special populations including the management of cancer-associated thrombosis, and the emerging promise of factor XI inhibition. In diagnostics, the field is moving beyond traditional methods with the investigation of novel biomarkers from proteomic and metabolomic studies and the clinical implementation of advanced imaging modalities like photon-counting CT, which offers superior resolution at lower radiation doses. Artificial intelligence is emerging as a transformative tool, potentially enhancing diagnostic accuracy in imaging. Ultimately, this review will assist clinicians in integrating evolving evidence with patient-centered decision-making to maximize benefit while minimizing harm and treating the diverse and common clinical problems of VTE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".