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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".