Inferior vena cava filter use and patient safety: legacy or science?
Bibliographic record
Abstract
There has been a dramatic increase in vena cava filter (VCF) use over the past 20 years in the absence of evidence that filters provide a net patient benefit or are required in most cases. This increase is largely attributable to the availability of retrievable filters and expanded indications, particularly as primary prophylaxis in patients thought to be at high risk of pulmonary embolism. Substantial variability in VCF use, unrelated to patient clinical factors, has been shown between hospitals, from region to region, and among various countries. Despite the lack of direct evidence for the benefit of VCFs for any indication, it is appropriate to insert a retrievable VCF in patients with a recent proximal deep vein thrombosis and an absolute contraindication to therapeutic anticoagulation and then to remove the filter once the bleeding risk decreases and the patient has been anticoagulated. Unfortunately, a high proportion of retrievable filters are not removed, even after the reason for their placement has long passed. Retrievable filters are associated with substantial rates of complications if they are not removed, including penetration of the vena caval wall, fracture and embolization of filter fragments, and caval occlusion. Patient safety priorities and medical-legal concerns mandate careful selection of patients for VCF placement and removal shortly after anticoagulation has been initiated.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".