Practice Patterns of Inferior Vena Cava Filter Placement and Factors That Predict Retrieval Rates: A Single-Center Institution and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: There is a wide variability in practice patterns on the use of inferior vena cava filters (IVCFs) among institutions, which is likely due to contrasting indication guidelines published by different professional societies. The aim of the present study is to report our healthcare system use of IVCF to: 1) determine practice patterns, 2) determine factors that may predict IVCF retrieval and 3) identify areas for improvement. METHODS: A retrospective review of 180 consecutive IVCF placement performed between July 2014 and December 2015 was conducted. RESULTS: One hundred nine (60.6%) IVCFs were placed for absolute indications, 27 (15.0%) for relative indications, 26 (14.4%) prophylactically and 18 (10.0%) for unknown indications. Average age was 59.3 years. Ninety-five had active cancer. Surgical and medical services requested filter placement in 112 (62.2%) and 68 (37.8%) patients, respectively. Thirteen (7.2%) patients had a hematology consult prior to IVCF placement. Documentation of the presence of an IVCF was present in 118/127 (92.9%) discharge summaries, and outlined instructions for filter retrieval post-discharge were present in 20/124 (16.1%) cases. Only 33 (25.0%) IVCF were retrieved at a median interval of 162 days (range: 4 - 1,053 days). None of the factors of interest was found to be significantly associated with IVCF retrieval. CONCLUSION: A root cause analysis identified that the lack of a structured system for IVCF tracking resulted in poor IVCF retrieval rates. This study resulted in the development of a hospital-initiated multidisciplinary team to address these issues.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".