The Value of Preprocedure Computed Tomography for Planning Insertion of Inferior Vena Cava Filters
Bibliographic record
Abstract
PURPOSE: To determine if valuable information could be obtained from abdominal computed tomography (CT) performed before insertion of an inferior vena cava (IVC) filter. MATERIALS AND METHODS: A retrospective review was performed on IVC filter insertions with a CT performed before the procedure. Cavagram and CT were compared for renal vein and IVC anatomy, the diameter of the IVC, and the prevalence of iliocaval thrombus. Correlations were assessed among 3 reference standards for measuring the IVC at cavography. RESULTS: The mean IVC diameter was 23.0 mm on CT. On cavagram the mean IVC diameter was assessed by using 3 reference standards: 20.7 mm, with the catheter tip as a reference; 26.9 mm, with a radiopaque ruler; and 23.4 mm, by using a lumbar vertebral body. There was good correlation among the 3 measures of IVC diameter (Pearson's r = 0.75, P < .0001) but moderate correlation with CT (r = 0.36-0.56, P < .001). The sensitivity of cavagram for detecting retroaortic and circumaortic renal veins was 40% and 0%, respectively. Nineteen accessory renal veins (12.8%) were not seen by cavagram. Thirteen patients (8.8%) had iliocaval thrombus on cavagram, of which 12 (92.3%) were not previously detected by CT. CONCLUSIONS: CT is more sensitive than cavagram for detection of renal vein variants and the level of the lowest renal vein. Therefore, if available, the CT should be reviewed before placement of an IVC filter to optimize positioning. Cavagram remains the criterion standard for detection of iliocaval thrombosis and is necessary before IVC filter insertion.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".