Vena caval filters: current knowledge, uncertainties and practical approaches
Bibliographic record
Abstract
PURPOSE OF REVIEW: Inferior vena caval (IVC) interruption has been used as a method to prevent pulmonary embolism since the 1940s. Despite an exponential increase in IVC filter use in both the treatment and prophylaxis of venous thromboembolism, there is little evidence to support current practice. This review will discuss controversies related to IVC filters and will provide a practical approach to their use. RECENT FINDINGS: Current practice guidelines recommend that IVC filters be placed in patients with acute proximal deep vein thrombosis and a contraindication to anticoagulation. We do not recommend IVC filters as primary thromboprophylaxis, even for high-risk surgical or trauma patients. We also do not believe that there is a role for IVC filters in cancer patients with venous thromboembolism when traditional anticoagulation has failed. IVC filters have been shown to be associated with an increased risk of recurrent deep vein thrombosis. SUMMARY: IVC filters are indicated in only a small proportion of patients who have venous thromboembolism. In these situations, retrievable filters are recommended. Anticoagulation should be initiated after filter placement as soon as it is safe to do so and the filter should then be removed shortly thereafter.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".