Radiation and Contrast Reduction Strategies in Endovascular Aneurysm Repair Procedures
Bibliographic record
Abstract
Background: The aim was to maximise reduction of radiation dose and intravenous contrast use in patients undergoing Endovascular Aneurysm Repair (EVAR) using current hybrid theatre technology. Methods: A combined retrospective and prospective study of patients undergoing EVAR using current technologies was performed. We developed and implemented dose reduction strategies (DRS) with three study groups, a. Pre-Hybrid, b. Post-hybrid installation pre-DRS implementation and, c. Post hybrid installation with DRS implementation. The pre-hybrid group was performed using a C-arm image intensifier (OEC 9900, GE) and post hybrid installation using a Discovery IGS 740 (GE Healthcare). DRS included use of fusion imaging, fluoroscopy frame rate reduction, extra low dose protocols, digital zoom and image collimation. All standard bifurcated endografts were included. Dose Area Product (DAP), procedure time, screening time and total intravenous contrast media used for each patient was recorded. Results: The mean DAP pre-hybrid was 41.67 Gycm2, which increased to 63.24 Gycm2 post-hybrid installation. This reduced to 36.57 Gycm2 after DRS implementation (43% reduction) despite an 8% increase in screening time in the post-DRS group (1218 secs vs 1118 secs). The contrast volume reduced from a mean of 80 ml of higher strength Niopam370 (Bracco, UK) intravenous contrast media pre-hybrid to 70.35 ml of lower strength Niopam300 (Bracco, UK) post-hybrid pre-DRS and to 54.19 ml after DRS implementation, an overall reduction of 32%. Conclusions: Current technologies alone may not result in radiation dose reduction. Developing DRS leads to significant reduction in DAP and contrast media volume. The benefit is reduction in radiation dose to patients and operators and contrast use reduction in patients. Publication History Article published online: 26 April 2021 © 2017. The Arab Journal of Interventional Radiology. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".