A Population-Based Study of Inferior Vena Cava Filters in Patients With Acute Venous Thromboembolism
Bibliographic record
Abstract
BACKGROUND: Our study objective was to describe the frequency, indications, and outcomes after inferior vena cava (IVC) filter placement in a population-based sample of residents of the Worcester, Massachusetts, metropolitan area who had been diagnosed as having acute venous thromboembolism (VTE) in 1999, 2001, and 2003. METHODS: A retrospective chart review of inpatient and outpatient medical records was conducted. Recorded indication(s) for IVC filter placement was determined among a subset of cases from 3 Worcester tertiary care hospitals. Three thrombosis specialists assessed the appropriateness of IVC filter placement. RESULTS: Of 1547 greater Worcester residents with validated acute VTE and without a prior IVC filter, 203 (13.1%) had an IVC filter placed after acute VTE. Patients with an IVC filter were older, had more comorbidities, and had a higher mortality rate during 3 years of follow-up. There was unanimous agreement by panel members that the use of an IVC filter was appropriate in 51% of cases and inappropriate in 26% of cases, with no consensus in the remaining 23%. CONCLUSIONS: In this community-based study, IVC filters were frequently used in the treatment of patients with acute VTE. Placement was deemed to be appropriate in approximately 50% of the patients but was not appropriate or debatable in the remaining cases. Given the increasing use of IVC filters, prospective studies are clearly needed to better define the indications for, and efficacy of, IVC filter placement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".