Treatment of venous thromboembolism in elderly patients in the era of direct oral anticoagulants
Bibliographic record
Abstract
The incidence of venous thromboembolism (VTE) and VTE‑related morbidity and mortality increase with advancing age. Over the past decade, substantial advances in the treatment of VTE have been achieved. Most notably, direct oral anticoagulants (DOACs) were introduced, which offer simple treatment regimens across a broad spectrum of patients with VTE and have become the first‑choice anticoagulants in many individuals in this population. Even though elderly patients are underrepresented in clinical trials, the extrapolation of overall study results to the elderly subpopulation can be considered justified regarding acute VTE treatment and the choice of anticoagulant agent. In the elderly, DOACs are not only associated with a lower bleeding risk but they also appear to be even more efficacious than vitamin K antagonists in preventing recurrent VTE during the acute treatment period. Determining the optimal treatment duration is the most challenging aspect of VTE management in elderly patients. The risk of bleeding increases with advancing age, and several risk factors for recurrent VTE after stopping anticoagulation are also more frequent in the elderly. Clinical decision rules estimating the risk of recurrent VTE and bleeding have limited utility in elderly patients. Shared decision making considering patients' preferences and values is therefore crucial to help determine individual treatment duration in these patients.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".