Implanted vascular access device related deep vein thrombosis in oncology patients: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Implanted vascular access devices (IVADs) have significantly improved the management of cancer patients. These patients are at an increased risk of venous thromboembolism and IVADs are a known risk factor. We sought to assess the incidence of IVAD-related upper extremity deep vein thrombosis (IVAD-related UEDVT) associated with BioFlo® IVADs (Angiodynamics, Inc.). METHODS: A total of 394 cancer patients were enrolled over 12 months. The primary outcome was the incidence of IVAD-related UEDVT confirmed by diagnostic imaging. IVAD-related UEDVT was defined as symptomatic ipsilateral upper extremity (axillary vein or proximal) deep vein thrombosis and symptomatic pulmonary embolism (PE). Patients were followed until initiation of therapeutic anticoagulation, catheter removal, death, or up to 12 months. RESULTS: 389 patients were included in the analysis. The median age of the cohort was 58.2 years; 68% (n = 273) were females. Sixty-six percent had gastrointestional cancer (including pancreatic cancer) and 68% had metastases. Eighty four percent of IVADs were right sided insertions. Ninety eight percent of catheter tip placements were distal superior vena cava (n = 237), cavo-atrial junction (n = 67) or atrium (n = 90). Overall, 5 patients had symptomatic IVAD-related UEDVT (1.29%, 95% CI 0.2 to 2.4%). CONCLUSION: IVAD-related UEDVT is an infrequent complication in cancer patients with BioFlo® IVADs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".