Predictors of inferior vena cava filter retrieval in a population-based Canadian cohort
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine the predictors of inferior vena cava (IVC) filter retrieval in a contemporary North American cohort of patients who received an IVC filter. METHODS: A retrospective population-based cohort study was conducted using Ontario administrative health data. Physician service fee codes were used to identify all adults with an IVC filter placement from January 1, 2010, to December 31, 2019. The cumulative incidence of filter retrieval over time was calculated, accounting for death as a competing risk. Multivariable sub-distribution hazard regression models were constructed to quantify the association between covariates and the likelihood of filter retrieval. RESULTS: A total of 5617 people received an IVC filter during the study period. Median follow-up was 1.8 years (interquartile range, 0.2-5.4 years). The probability of filter retrieval plateaued under 40% with most retrievals (96%; n = 2049 of 2135) occurring within 1 year of placement. Filter placement in a teaching hospital (hazard ratio, 1.85; 95% confidence interval, 1.60-2.02), and placement after 2016 were associated with a greater likelihood of filter retrieval. Older age and greater comorbidity were associated with a lower likelihood of filter retrieval. CONCLUSIONS: In this population-based study of IVC filter retrieval, less than 40% of filters were retrieved, mostly within 1 year of insertion. Better coordination and standardization of services responsible for follow-up of patients with IVC filters are needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".