Inferior vena cava filter use at a large community hospital: a retrospective cohort study
Bibliographic record
Abstract
Inferior vena cava (IVC) filters are considered when patients with venous thromboembolism (VTE) develop a contraindication to anticoagulation. Use of IVC filters is increasing, despite associated complications and lack of data on efficacy in reducing VTE-related mortality. We characterized the pattern of IVC filter use at a large community hospital between 2018 and 2022. Specifically, we assessed the indications for IVC filter insertion, filter removal rates, and filter-associated complications. Indications for IVC filters were compared to those outlined by current clinical practice guidelines. We reviewed 120 consecutive filter placement events. The most common indications included recent VTE and active bleeding (40.0%) or need for anticoagulation interruption for surgery (25.8%). Approximately one-third (30.0%) of IVC filters were inserted for indications either not supported or addressed by guidelines. Half (50.0%) of patients had successful removal of their IVC filter. At least 13 patients (10.8%) experienced a filter-related complication. In a large community-based practice, nearly one-third of IVC filters were inserted for indications not universally supported by current practice guidelines. Moreover, most IVC filters were not removed, raising the risk of filter-associated complications, and supporting the need for development of comprehensive guidelines addressing use of IVC filters, and post-insertion monitoring practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".